diff --git a/data/2024-04-12_leung/1/adapters.fasta b/data/2024-04-12_leung/1/adapters.fasta
new file mode 100644
index 0000000..c993fc0
--- /dev/null
+++ b/data/2024-04-12_leung/1/adapters.fasta
@@ -0,0 +1,41 @@
+>0
+TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG
+>1
+GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG
+>2
+CTGTCTCTTATACACATCTCCGAGCCCACGAGAC
+>3
+unspecified
+>4
+GATCGGAAGAGCGGTTCAGCAGGAATGCCGAG
+>5
+AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
+>6
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
+>7
+CTGTCTCTTATACACATCTGACGCTGCCGACGA
+>8
+CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATC
+T
+>9
+TGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
+>10
+CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT
+>11
+ACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>12
+AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
+>13
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGATC
+>14
+GATCGGAAGAGCACACGTCTGAACTCCAGTCAC
+>15
+CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT
+>16
+CAAGCAGAAGACGGCATACGAGAT
+>17
+AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>18
+heifigepsna
+>19
+GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG
diff --git a/data/2024-04-12_leung/1/bracken_counts.tsv b/data/2024-04-12_leung/1/bracken_counts.tsv
new file mode 100644
index 0000000..a091a15
--- /dev/null
+++ b/data/2024-04-12_leung/1/bracken_counts.tsv
@@ -0,0 +1,401 @@
+name	taxonomy_id	taxonomy_lvl	kraken_assigned_reads	added_reads	new_est_reads	fraction_total_reads	sample
+Eukaryota	2759	D	628649	846	629495	0.50701	SRR10002747
+Bacteria	2	D	610755	127	610882	0.49202	SRR10002747
+Archaea	2157	D	874	0	874	7e-4	SRR10002747
+Viruses	10239	D	312	12	324	2.6e-4	SRR10002747
+Bacteria	2	D	645143	227	645370	0.67442	SRR10002760
+Eukaryota	2759	D	307163	689	307852	0.32171	SRR10002760
+Archaea	2157	D	3325	1	3326	0.00348	SRR10002760
+Viruses	10239	D	368	7	375	3.9e-4	SRR10002760
+Eukaryota	2759	D	943543	950	944493	0.54109	SRR10002752
+Bacteria	2	D	797023	197	797220	0.45672	SRR10002752
+Archaea	2157	D	3386	0	3386	0.00194	SRR10002752
+Viruses	10239	D	443	7	450	2.6e-4	SRR10002752
+Bacteria	2	D	680400	300	680700	0.61376	SRR10002699
+Eukaryota	2759	D	426007	737	426744	0.38478	SRR10002699
+Archaea	2157	D	881	0	881	7.9e-4	SRR10002699
+Viruses	10239	D	726	16	742	6.7e-4	SRR10002699
+Bacteria	2	D	697509	209	697718	0.60802	SRR10002726
+Eukaryota	2759	D	446733	741	447474	0.38995	SRR10002726
+Archaea	2157	D	2010	0	2010	0.00175	SRR10002726
+Viruses	10239	D	305	18	323	2.8e-4	SRR10002726
+Bacteria	2	D	1547817	600	1548417	0.81416	SRR10002737
+Eukaryota	2759	D	348050	1158	349208	0.18361	SRR10002737
+Archaea	2157	D	3818	2	3820	0.00201	SRR10002737
+Viruses	10239	D	413	8	421	2.2e-4	SRR10002737
+Bacteria	2	D	423832	113	423945	0.5327	SRR10002711
+Eukaryota	2759	D	370273	429	370702	0.4658	SRR10002711
+Archaea	2157	D	1018	0	1018	0.00128	SRR10002711
+Viruses	10239	D	164	6	170	2.1e-4	SRR10002711
+Eukaryota	2759	D	881551	782	882333	0.64056	SRR10002704
+Bacteria	2	D	494069	105	494174	0.35876	SRR10002704
+Archaea	2157	D	695	0	695	5e-4	SRR10002704
+Viruses	10239	D	230	1	231	1.7e-4	SRR10002704
+Bacteria	2	D	1247006	487	1247493	0.6594	SRR10002731
+Eukaryota	2759	D	640257	1351	641608	0.33914	SRR10002731
+Archaea	2157	D	2330	1	2331	0.00123	SRR10002731
+Viruses	10239	D	347	77	424	2.2e-4	SRR10002731
+Eukaryota	2759	D	799507	727	800234	0.55472	SRR10002740
+Bacteria	2	D	639247	193	639440	0.44326	SRR10002740
+Archaea	2157	D	1887	0	1887	0.00131	SRR10002740
+Viruses	10239	D	1017	17	1034	7.2e-4	SRR10002740
+Eukaryota	2759	D	1386259	1166	1387425	0.62982	SRR10002703
+Bacteria	2	D	813892	221	814113	0.36956	SRR10002703
+Archaea	2157	D	898	0	898	4.1e-4	SRR10002703
+Viruses	10239	D	454	9	463	2.1e-4	SRR10002703
+Eukaryota	2759	D	1208737	628	1209365	0.6341	SRR10002698
+Bacteria	2	D	696363	297	696660	0.36527	SRR10002698
+Archaea	2157	D	416	0	416	2.2e-4	SRR10002698
+Viruses	10239	D	665	114	779	4.1e-4	SRR10002698
+Bacteria	2	D	839069	119	839188	0.50766	SRR10002684
+Eukaryota	2759	D	811732	619	812351	0.49142	SRR10002684
+Archaea	2157	D	696	0	696	4.2e-4	SRR10002684
+Viruses	10239	D	723	103	826	5e-4	SRR10002684
+Eukaryota	2759	D	1958028	1167	1959195	0.58634	SRR10002772
+Bacteria	2	D	1379465	347	1379812	0.41295	SRR10002772
+Archaea	2157	D	1463	0	1463	4.4e-4	SRR10002772
+Viruses	10239	D	891	10	901	2.7e-4	SRR10002772
+Eukaryota	2759	D	783160	551	783711	0.52419	SRR10002692
+Bacteria	2	D	710035	376	710411	0.47517	SRR10002692
+Archaea	2157	D	452	0	452	3e-4	SRR10002692
+Viruses	10239	D	449	56	505	3.4e-4	SRR10002692
+Eukaryota	2759	D	1099460	868	1100328	0.6034	SRR10002748
+Bacteria	2	D	721681	241	721922	0.39589	SRR10002748
+Archaea	2157	D	704	0	704	3.9e-4	SRR10002748
+Viruses	10239	D	578	23	601	3.3e-4	SRR10002748
+Bacteria	2	D	750960	357	751317	0.51188	SRR10002751
+Eukaryota	2759	D	713349	810	714159	0.48656	SRR10002751
+Archaea	2157	D	1519	0	1519	0.00103	SRR10002751
+Viruses	10239	D	731	44	775	5.3e-4	SRR10002751
+Eukaryota	2759	D	930747	1068	931815	0.54011	SRR10002761
+Bacteria	2	D	791078	307	791385	0.45871	SRR10002761
+Archaea	2157	D	1668	0	1668	9.7e-4	SRR10002761
+Viruses	10239	D	339	17	356	2.1e-4	SRR10002761
+Bacteria	2	D	1137395	423	1137818	0.67629	SRR10002764
+Eukaryota	2759	D	540425	907	541332	0.32176	SRR10002764
+Archaea	2157	D	2728	1	2729	0.00162	SRR10002764
+Viruses	10239	D	534	18	552	3.3e-4	SRR10002764
+Bacteria	2	D	1351953	277	1352230	0.54357	SRR10002725
+Eukaryota	2759	D	1130802	1103	1131905	0.455	SRR10002725
+Archaea	2157	D	2953	0	2953	0.00119	SRR10002725
+Viruses	10239	D	597	5	602	2.4e-4	SRR10002725
+Bacteria	2	D	688960	144	689104	0.53483	SRR10002705
+Eukaryota	2759	D	596790	620	597410	0.46366	SRR10002705
+Archaea	2157	D	1696	0	1696	0.00132	SRR10002705
+Viruses	10239	D	236	10	246	1.9e-4	SRR10002705
+Eukaryota	2759	D	2252516	1000	2253516	0.78035	SRR10002768
+Bacteria	2	D	633370	99	633469	0.21936	SRR10002768
+Archaea	2157	D	426	0	426	1.5e-4	SRR10002768
+Viruses	10239	D	404	0	404	1.4e-4	SRR10002768
+Bacteria	2	D	1095202	207	1095409	0.5382	SRR10002681
+Eukaryota	2759	D	932314	1311	933625	0.45871	SRR10002681
+Archaea	2157	D	5572	2	5574	0.00274	SRR10002681
+Viruses	10239	D	724	3	727	3.6e-4	SRR10002681
+Bacteria	2	D	513353	277	513630	0.84433	SRR10002694
+Eukaryota	2759	D	92484	630	93114	0.15307	SRR10002694
+Archaea	2157	D	1398	1	1399	0.0023	SRR10002694
+Viruses	10239	D	177	5	182	3e-4	SRR10002694
+Eukaryota	2759	D	1512741	1050	1513791	0.5684	SRR10002773
+Bacteria	2	D	1146428	368	1146796	0.4306	SRR10002773
+Archaea	2157	D	1233	0	1233	4.6e-4	SRR10002773
+Viruses	10239	D	1400	6	1406	5.3e-4	SRR10002773
+Bacteria	2	D	799213	355	799568	0.75145	SRR10002759
+Eukaryota	2759	D	262284	860	263144	0.24731	SRR10002759
+Archaea	2157	D	986	0	986	0.00093000000000000005	SRR10002759
+Viruses	10239	D	317	13	330	3.1e-4	SRR10002759
+Bacteria	2	D	448278	202	448480	0.64225	SRR10002750
+Eukaryota	2759	D	246535	570	247105	0.35387	SRR10002750
+Archaea	2157	D	2497	1	2498	0.00358	SRR10002750
+Viruses	10239	D	187	23	210	3e-4	SRR10002750
+Eukaryota	2759	D	2160896	1088	2161984	0.62625	SRR10002771
+Bacteria	2	D	1286727	353	1287080	0.37282	SRR10002771
+Archaea	2157	D	892	0	892	2.6e-4	SRR10002771
+Viruses	10239	D	2304	32	2336	6.8e-4	SRR10002771
+Bacteria	2	D	491439	184	491623	0.60764	SRR10002707
+Eukaryota	2759	D	315388	570	315958	0.39052	SRR10002707
+Archaea	2157	D	1337	0	1337	0.00165	SRR10002707
+Viruses	10239	D	129	25	154	1.9e-4	SRR10002707
+Bacteria	2	D	440784	163	440947	0.81836	SRR10002702
+Eukaryota	2759	D	96321	628	96949	0.17993	SRR10002702
+Archaea	2157	D	780	1	781	0.00145	SRR10002702
+Viruses	10239	D	141	1	142	2.6e-4	SRR10002702
+Eukaryota	2759	D	1696779	931	1697710	0.60946	SRR10002766
+Bacteria	2	D	1085917	260	1086177	0.38993	SRR10002766
+Archaea	2157	D	921	0	921	3.3e-4	SRR10002766
+Viruses	10239	D	775	7	782	2.8e-4	SRR10002766
+Eukaryota	2759	D	1547872	1541	1549413	0.57446	SRR10002715
+Bacteria	2	D	1145067	395	1145462	0.42469	SRR10002715
+Archaea	2157	D	1515	0	1515	5.6e-4	SRR10002715
+Viruses	10239	D	694	67	761	2.8e-4	SRR10002715
+Eukaryota	2759	D	900876	1151	902027	0.52985	SRR10002720
+Bacteria	2	D	798926	258	799184	0.46944	SRR10002720
+Archaea	2157	D	835	0	835	4.9e-4	SRR10002720
+Viruses	10239	D	367	3	370	2.2e-4	SRR10002720
+Bacteria	2	D	743309	286	743595	0.63308	SRR10002719
+Eukaryota	2759	D	429089	640	429729	0.36586	SRR10002719
+Archaea	2157	D	931	0	931	7.9e-4	SRR10002719
+Viruses	10239	D	290	17	307	2.6e-4	SRR10002719
+Eukaryota	2759	D	1727108	896	1728004	0.6328	SRR10002765
+Bacteria	2	D	1000733	217	1000950	0.36655	SRR10002765
+Archaea	2157	D	1012	0	1012	3.7e-4	SRR10002765
+Viruses	10239	D	778	2	780	2.9e-4	SRR10002765
+Bacteria	2	D	1260090	449	1260539	0.50979	SRR10002714
+Eukaryota	2759	D	1206857	1439	1208296	0.48866	SRR10002714
+Archaea	2157	D	2954	0	2954	0.00119	SRR10002714
+Viruses	10239	D	873	23	896	3.6e-4	SRR10002714
+Bacteria	2	D	460375	205	460580	0.54277	SRR10002723
+Eukaryota	2759	D	384214	724	384938	0.45363	SRR10002723
+Archaea	2157	D	2812	1	2813	0.00331	SRR10002723
+Viruses	10239	D	225	16	241	2.8e-4	SRR10002723
+Eukaryota	2759	D	1546306	837	1547143	0.56696	SRR10002727
+Bacteria	2	D	1178845	325	1179170	0.43212	SRR10002727
+Archaea	2157	D	1809	0	1809	6.6e-4	SRR10002727
+Viruses	10239	D	686	10	696	2.6e-4	SRR10002727
+Bacteria	2	D	1439979	416	1440395	0.54023	SRR10002733
+Eukaryota	2759	D	1222546	1010	1223556	0.45891	SRR10002733
+Archaea	2157	D	1404	0	1404	5.3e-4	SRR10002733
+Viruses	10239	D	874	10	884	3.3e-4	SRR10002733
+Bacteria	2	D	1998972	370	1999342	0.59273	SRR10002678
+Eukaryota	2759	D	1369980	1177	1371157	0.40649	SRR10002678
+Archaea	2157	D	1339	0	1339	4e-4	SRR10002678
+Viruses	10239	D	1279	5	1284	3.8e-4	SRR10002678
+Eukaryota	2759	D	3039642	1222	3040864	0.85061	SRR10002770
+Bacteria	2	D	533355	129	533484	0.14923	SRR10002770
+Archaea	2157	D	296	0	296	8e-5	SRR10002770
+Viruses	10239	D	267	1	268	7e-5	SRR10002770
+Bacteria	2	D	553135	138	553273	0.58918	SRR10002712
+Eukaryota	2759	D	383335	590	383925	0.40884	SRR10002712
+Archaea	2157	D	1638	0	1638	0.00174	SRR10002712
+Viruses	10239	D	210	2	212	2.3e-4	SRR10002712
+Bacteria	2	D	845781	8467	854248	0.98487	SRR10002688
+Eukaryota	2759	D	12921	11	12932	0.01491	SRR10002688
+Archaea	2157	D	13	0	13	1e-5	SRR10002688
+Viruses	10239	D	13	162	175	2e-4	SRR10002688
+Bacteria	2	D	943795	288	944083	0.7035	SRR10002717
+Eukaryota	2759	D	395680	716	396396	0.29538	SRR10002717
+Archaea	2157	D	1137	0	1137	8.5e-4	SRR10002717
+Viruses	10239	D	341	14	355	2.6e-4	SRR10002717
+Bacteria	2	D	912720	423	913143	0.61133	SRR10002722
+Eukaryota	2759	D	577483	964	578447	0.38725	SRR10002722
+Archaea	2157	D	1720	0	1720	0.00115	SRR10002722
+Viruses	10239	D	382	17	399	2.7e-4	SRR10002722
+Bacteria	2	D	894613	340	894953	0.53304	SRR10002701
+Eukaryota	2759	D	781289	957	782246	0.46591	SRR10002701
+Archaea	2157	D	929	0	929	5.5e-4	SRR10002701
+Viruses	10239	D	812	24	836	5e-4	SRR10002701
+Bacteria	2	D	596387	172	596559	0.67465	SRR10002710
+Eukaryota	2759	D	283481	649	284130	0.32132	SRR10002710
+Archaea	2157	D	3276	1	3277	0.00371	SRR10002710
+Viruses	10239	D	277	6	283	3.2e-4	SRR10002710
+Eukaryota	2759	D	1078557	680	1079237	0.52948	SRR10002683
+Bacteria	2	D	956848	208	957056	0.46953	SRR10002683
+Archaea	2157	D	1051	0	1051	5.2e-4	SRR10002683
+Viruses	10239	D	958	3	961	4.7e-4	SRR10002683
+Eukaryota	2759	D	2022033	663	2022696	0.86846	SRR10002769
+Bacteria	2	D	305927	35	305962	0.13137	SRR10002769
+Archaea	2157	D	217	0	217	9e-5	SRR10002769
+Viruses	10239	D	194	2	196	8e-5	SRR10002769
+Bacteria	2	D	465896	209	466105	0.53287	SRR10002741
+Eukaryota	2759	D	404343	670	405013	0.46302	SRR10002741
+Archaea	2157	D	3381	1	3382	0.00387	SRR10002741
+Viruses	10239	D	195	15	210	2.4e-4	SRR10002741
+Eukaryota	2759	D	2715830	1000	2716830	0.69138	SRR10002687
+Bacteria	2	D	1210799	182	1210981	0.30817	SRR10002687
+Archaea	2157	D	1123	0	1123	2.9e-4	SRR10002687
+Viruses	10239	D	654	8	662	1.7e-4	SRR10002687
+Eukaryota	2759	D	1602111	843	1602954	0.63754	SRR10002680
+Bacteria	2	D	909542	144	909686	0.36181	SRR10002680
+Archaea	2157	D	993	0	993	3.9e-4	SRR10002680
+Viruses	10239	D	657	1	658	2.6e-4	SRR10002680
+Eukaryota	2759	D	1269117	717	1269834	0.61285	SRR10002690
+Bacteria	2	D	799876	216	800092	0.38614	SRR10002690
+Archaea	2157	D	1592	0	1592	7.7e-4	SRR10002690
+Viruses	10239	D	500	10	510	2.5e-4	SRR10002690
+Bacteria	2	D	879133	288	879421	0.5014	SRR10002718
+Eukaryota	2759	D	868586	1119	869705	0.49586	SRR10002718
+Archaea	2157	D	4211	1	4212	0.0024	SRR10002718
+Viruses	10239	D	568	9	577	3.3e-4	SRR10002718
+Bacteria	2	D	1397470	376	1397846	0.50116	SRR10002732
+Eukaryota	2759	D	1386870	1008	1387878	0.49759	SRR10002732
+Archaea	2157	D	2078	0	2078	7.5e-4	SRR10002732
+Viruses	10239	D	1376	14	1390	5e-4	SRR10002732
+Bacteria	2	D	1943132	373	1943505	0.62719	SRR10002679
+Eukaryota	2759	D	1148169	1447	1149616	0.37099	SRR10002679
+Archaea	2157	D	4867	1	4868	0.00157	SRR10002679
+Viruses	10239	D	757	8	765	2.5e-4	SRR10002679
+Eukaryota	2759	D	640496	552	641048	0.59165	SRR10002696
+Bacteria	2	D	441169	163	441332	0.40733	SRR10002696
+Archaea	2157	D	739	0	739	6.8e-4	SRR10002696
+Viruses	10239	D	360	4	364	3.4e-4	SRR10002696
+Bacteria	2	D	622050	206	622256	0.53884	SRR10002763
+Eukaryota	2759	D	530554	658	531212	0.46	SRR10002763
+Archaea	2157	D	932	0	932	8.1e-4	SRR10002763
+Viruses	10239	D	403	5	408	3.5e-4	SRR10002763
+Bacteria	2	D	757362	226	757588	0.62781	SRR10002708
+Eukaryota	2759	D	446586	933	447519	0.37085	SRR10002708
+Archaea	2157	D	1353	0	1353	0.00112	SRR10002708
+Viruses	10239	D	240	21	261	2.2e-4	SRR10002708
+Bacteria	2	D	1184415	272	1184687	0.63928	SRR10002734
+Eukaryota	2759	D	660390	1130	661520	0.35697	SRR10002734
+Archaea	2157	D	6056	2	6058	0.00327	SRR10002734
+Viruses	10239	D	899	4	903	4.9e-4	SRR10002734
+Eukaryota	2759	D	1695354	881	1696235	0.68165	SRR10002685
+Bacteria	2	D	790626	198	790824	0.3178	SRR10002685
+Archaea	2157	D	719	0	719	2.9e-4	SRR10002685
+Viruses	10239	D	639	1	640	2.6e-4	SRR10002685
+Eukaryota	2759	D	778573	840	779413	0.55794	SRR10002738
+Bacteria	2	D	615020	167	615187	0.44038	SRR10002738
+Archaea	2157	D	2064	0	2064	0.00148	SRR10002738
+Viruses	10239	D	265	18	283	2e-4	SRR10002738
+Eukaryota	2759	D	1223873	797	1224670	0.51813	SRR10002686
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+Viruses	10239	D	815	10	825	3.5e-4	SRR10002686
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+Eukaryota	2759	D	725641	515	726156	0.43137	SRR10002693
+Archaea	2157	D	467	0	467	2.8e-4	SRR10002693
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+Eukaryota	2759	D	51849	572	52421	0.08446	SRR10002689
+Archaea	2157	D	1707	3	1710	0.00276	SRR10002689
+Viruses	10239	D	204	3	207	3.3e-4	SRR10002689
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+Eukaryota	2759	D	915958	1229	917187	0.45901	SRR10002774
+Archaea	2157	D	1279	0	1279	6.4e-4	SRR10002774
+Viruses	10239	D	933	6	939	4.7e-4	SRR10002774
+Eukaryota	2759	D	751215	809	752024	0.5451	SRR10002739
+Bacteria	2	D	626008	159	626167	0.45388	SRR10002739
+Archaea	2157	D	1005	0	1005	7.3e-4	SRR10002739
+Viruses	10239	D	394	9	403	2.9e-4	SRR10002739
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+Eukaryota	2759	D	1335005	915	1335920	0.48205	SRR10002735
+Archaea	2157	D	1327	0	1327	4.8e-4	SRR10002735
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+Eukaryota	2759	D	1071557	1232	1072789	0.50359	SRR10002716
+Bacteria	2	D	1055282	371	1055653	0.49554	SRR10002716
+Archaea	2157	D	1211	0	1211	5.7e-4	SRR10002716
+Viruses	10239	D	625	15	640	3e-4	SRR10002716
+Bacteria	2	D	712912	236	713148	0.59343	SRR10002713
+Eukaryota	2759	D	483508	835	484343	0.40304	SRR10002713
+Archaea	2157	D	3876	1	3877	0.00323	SRR10002713
+Viruses	10239	D	355	12	367	3.1e-4	SRR10002713
+Eukaryota	2759	D	1342721	858	1343579	0.64985	SRR10002754
+Bacteria	2	D	721798	169	721967	0.3492	SRR10002754
+Archaea	2157	D	1463	0	1463	7.1e-4	SRR10002754
+Viruses	10239	D	481	23	504	2.4e-4	SRR10002754
+Bacteria	2	D	851577	349	851926	0.60275	SRR10002762
+Eukaryota	2759	D	557790	893	558683	0.39527	SRR10002762
+Archaea	2157	D	2395	1	2396	0.0017	SRR10002762
+Viruses	10239	D	387	14	401	2.8e-4	SRR10002762
+Bacteria	2	D	1173856	259	1174115	0.52212	SRR10002729
+Eukaryota	2759	D	1072037	804	1072841	0.47709	SRR10002729
+Archaea	2157	D	928	0	928	4.1e-4	SRR10002729
+Viruses	10239	D	833	16	849	3.8e-4	SRR10002729
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+Eukaryota	2759	D	1319763	1012	1320775	0.44868	SRR10002724
+Archaea	2157	D	1945	0	1945	6.6e-4	SRR10002724
+Viruses	10239	D	1026	18	1044	3.5e-4	SRR10002724
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+Archaea	2157	D	5567	3	5570	0.00416	SRR10002757
+Viruses	10239	D	321	9	330	2.5e-4	SRR10002757
+Bacteria	2	D	304292	92	304384	0.51999	SRR10002746
+Eukaryota	2759	D	279699	422	280121	0.47854	SRR10002746
+Archaea	2157	D	393	0	393	6.7e-4	SRR10002746
+Viruses	10239	D	463	1	464	7.9e-4	SRR10002746
+Eukaryota	2759	D	1431683	1381	1433064	0.65503	SRR10002676
+Bacteria	2	D	752846	270	753116	0.34424	SRR10002676
+Archaea	2157	D	785	0	785	3.6e-4	SRR10002676
+Viruses	10239	D	784	27	811	3.7e-4	SRR10002676
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+Eukaryota	2759	D	31662	376	32038	0.0551	SRR10002709
+Archaea	2157	D	1008	2	1010	0.00174	SRR10002709
+Viruses	10239	D	142	25	167	2.9e-4	SRR10002709
+Eukaryota	2759	D	1077738	1311	1079049	0.54157	SRR10002677
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+Archaea	2157	D	1006	0	1006	5e-4	SRR10002677
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+Eukaryota	2759	D	747785	630	748415	0.60704	SRR10002745
+Bacteria	2	D	483055	100	483155	0.39189	SRR10002745
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+Eukaryota	2759	D	397279	743	398022	0.26759	SRR10002721
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+Viruses	10239	D	326	34	360	2.4e-4	SRR10002721
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+Eukaryota	2759	D	356414	767	357181	0.38905	SRR10002742
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+Eukaryota	2759	D	457167	777	457944	0.32611	SRR10002755
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diff --git a/data/2024-04-12_leung/1/sample-metadata.csv b/data/2024-04-12_leung/1/sample-metadata.csv
new file mode 100644
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+++ b/data/2024-04-12_leung/1/sample-metadata.csv
@@ -0,0 +1,101 @@
+sample,library,country,region,city,location,instrument,date
+SRR10002675,SRR10002675,Sweden,Europe,Stockholm,"Stockholm, T-centralen",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002676,SRR10002676,Sweden,Europe,Stockholm,"Stockholm, Slussen",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002677,SRR10002677,Sweden,Europe,Stockholm,"Stockholm, Medborgarplatsen",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002678,SRR10002678,Hong Kong,Asia,Hong Kong,"Hong Kong, Admiralty",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002679,SRR10002679,Hong Kong,Asia,Hong Kong,"Hong Kong, Ocean Park",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002680,SRR10002680,Hong Kong,Asia,Hong Kong,"Hong Kong, Fortress Hill",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002681,SRR10002681,Hong Kong,Asia,Hong Kong,"Hong Kong, North Point",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002682,SRR10002682,Hong Kong,Asia,Hong Kong,"Hong Kong, Quarry Bay",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002683,SRR10002683,Hong Kong,Asia,Hong Kong,"Hong Kong, Yau Tong",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002684,SRR10002684,Hong Kong,Asia,Hong Kong,"Hong Kong, Tsing Yi",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002685,SRR10002685,Hong Kong,Asia,Hong Kong,"Hong Kong, Lai King",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002686,SRR10002686,Hong Kong,Asia,Hong Kong,"Hong Kong, Cheung Sha Wan",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002687,SRR10002687,Hong Kong,Asia,Hong Kong,"Hong Kong, Sham Shui Po",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002688,SRR10002688,uncalculated,uncalculated,,"Hong Kong, Sham Shui Po",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002689,SRR10002689,USA,North America,Denver,"Denver, Denver Airport",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002690,SRR10002690,USA,North America,Denver,"Denver, Train Cabin - Back wagon",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002691,SRR10002691,USA,North America,Denver,"Denver, Boulder Junction",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002692,SRR10002692,USA,North America,Denver,"Denver, Boulder Junction",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002693,SRR10002693,USA,North America,Denver,"Denver, Boulder Junction",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002694,SRR10002694,USA,North America,Denver,"Denver, Denver Airport",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002695,SRR10002695,Norway,Europe,Oslo,"Oslo, Nydalen",Bobcat ACD-200 electret filter air sampler,2017-06-21
+SRR10002696,SRR10002696,Norway,Europe,Oslo,"Oslo, Loeren",Bobcat ACD-200 electret filter air sampler,2017-06-21
+SRR10002697,SRR10002697,USA,North America,Denver,"Denver, Boulder Junction",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002698,SRR10002698,USA,North America,Denver,"Denver, Boulder Junction",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002699,SRR10002699,United Kingdom,Europe,London,"London, Warren Street",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002700,SRR10002700,United Kingdom,Europe,London,"London, Warren Street",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002701,SRR10002701,United Kingdom,Europe,London,"London, Green Park",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002702,SRR10002702,Norway,Europe,Oslo,"Oslo, Forskningsparken",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002703,SRR10002703,United Kingdom,Europe,London,"London, Green Park",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002704,SRR10002704,Norway,Europe,Oslo,"Oslo, Majorstua",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002705,SRR10002705,Norway,Europe,Oslo,"Oslo, Toeyen",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002706,SRR10002706,Norway,Europe,Oslo,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002707,SRR10002707,Norway,Europe,Oslo,"Oslo, Lindeberg",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002708,SRR10002708,Norway,Europe,Oslo,"Oslo, Ellingsrudaasen",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002709,SRR10002709,Norway,Europe,Oslo,"Oslo, Montebello",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002710,SRR10002710,Norway,Europe,Oslo,"Oslo, Nationaltheateret",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002711,SRR10002711,Norway,Europe,Oslo,"Oslo, Stortinget",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002712,SRR10002712,Norway,Europe,Oslo,"Oslo, Jernbanetorget",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002713,SRR10002713,United Kingdom,Europe,London,"London, Finsbury Park",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002714,SRR10002714,United Kingdom,Europe,London,"London, Highbury & Islington",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002715,SRR10002715,United Kingdom,Europe,London,"London, Kings Cross St Pancras",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002716,SRR10002716,United Kingdom,Europe,London,"London, Kings Cross St Pancras",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002717,SRR10002717,United Kingdom,Europe,London,"London, Warren Street",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002718,SRR10002718,United Kingdom,Europe,London,"London, Finsbury Park",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002719,SRR10002719,United Kingdom,Europe,London,"London, Pimlico",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002720,SRR10002720,United Kingdom,Europe,London,"London, Pimlico",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002721,SRR10002721,United Kingdom,Europe,London,"London, Vauxhall",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002722,SRR10002722,United Kingdom,Europe,London,"London, Vauxhall",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002723,SRR10002723,Norway,Europe,Oslo,"Oslo, Lindeberg",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002724,SRR10002724,Hong Kong,Asia,Hong Kong,"Hong Kong, Admiralty",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002725,SRR10002725,Hong Kong,Asia,Hong Kong,"Hong Kong, Ocean Park",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002726,SRR10002726,Norway,Europe,Oslo,"Oslo, Stortinget",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002727,SRR10002727,Hong Kong,Asia,Hong Kong,"Hong Kong, Cheung Sha Wan",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002728,SRR10002728,Hong Kong,Asia,Hong Kong,"Hong Kong, Sham Shui Po",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002729,SRR10002729,Hong Kong,Asia,Hong Kong,"Hong Kong, Tsing Yi",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002730,SRR10002730,Hong Kong,Asia,Hong Kong,"Hong Kong, Lai King",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002731,SRR10002731,Norway,Europe,Oslo,"Oslo, Carl Berners plass",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002732,SRR10002732,Hong Kong,Asia,Hong Kong,"Hong Kong, Yau Tong",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002733,SRR10002733,Hong Kong,Asia,Hong Kong,"Hong Kong, Fortress Hill",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002734,SRR10002734,Hong Kong,Asia,Hong Kong,"Hong Kong, North Point",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002735,SRR10002735,Hong Kong,Asia,Hong Kong,"Hong Kong, Tsim Sha Tsui",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002736,SRR10002736,Norway,Europe,Oslo,"Oslo, Toeyen",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002737,SRR10002737,USA,North America,New York City,"New York City, 63rd St.",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002738,SRR10002738,Norway,Europe,Oslo,"Oslo, Stortinget",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002739,SRR10002739,Norway,Europe,Oslo,"Oslo, Carl Berners plass",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002740,SRR10002740,Norway,Europe,Oslo,"Oslo, Groenland",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002741,SRR10002741,Norway,Europe,Oslo,"Oslo, Ellingsrudaasen",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002742,SRR10002742,Norway,Europe,Oslo,"Oslo, Nationaltheateret",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002743,SRR10002743,USA,North America,New York City,"New York City, 72nd St.",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002744,SRR10002744,Norway,Europe,Oslo,"Oslo, Montebello",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002745,SRR10002745,Norway,Europe,Oslo,"Oslo, Nationaltheateret",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002746,SRR10002746,Norway,Europe,Oslo,"Oslo, Montebello",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002747,SRR10002747,Norway,Europe,Oslo,"Oslo, Nydalen",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002748,SRR10002748,Norway,Europe,Oslo,"Oslo, Loeren",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002749,SRR10002749,Norway,Europe,Oslo,"Oslo, Vestli",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002750,SRR10002750,Norway,Europe,Oslo,"Oslo, Romsaas",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002751,SRR10002751,Norway,Europe,Oslo,"Oslo, Carl Berners plass",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002752,SRR10002752,Norway,Europe,Oslo,"Oslo, Groenland",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002753,SRR10002753,Norway,Europe,Oslo,"Oslo, Jernbanetorget",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002754,SRR10002754,Norway,Europe,Oslo,"Oslo, Stortinget",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002755,SRR10002755,United Kingdom,Europe,London,"London, Warren Street",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002756,SRR10002756,United Kingdom,Europe,London,"London, Oxford Circus",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002757,SRR10002757,United Kingdom,Europe,London,"London, Finsbury Park",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002758,SRR10002758,United Kingdom,Europe,London,"London, Warren Street",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002759,SRR10002759,United Kingdom,Europe,London,"London, Brixton",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002760,SRR10002760,United Kingdom,Europe,London,"London, Finsbury Park",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002761,SRR10002761,United Kingdom,Europe,London,"London, Highbury & Islington",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002762,SRR10002762,United Kingdom,Europe,London,"London, Highbury & Islington",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002763,SRR10002763,United Kingdom,Europe,London,"London, Vauxhall",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002764,SRR10002764,United Kingdom,Europe,London,"London, Highbury & Islington",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002765,SRR10002765,Hong Kong,Asia,Hong Kong,"Hong Kong, Shek Kip Mei",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002766,SRR10002766,Hong Kong,Asia,Hong Kong,"Hong Kong, Prince Edward",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002767,SRR10002767,Hong Kong,Asia,Hong Kong,"Hong Kong, Kowloon Tong",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002768,SRR10002768,Hong Kong,Asia,Hong Kong,"Hong Kong, Tai Wai",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002769,SRR10002769,Hong Kong,Asia,Hong Kong,"Hong Kong, Che Kung Temple",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002770,SRR10002770,Hong Kong,Asia,Hong Kong,"Hong Kong, Sha Tin Wai",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002771,SRR10002771,Hong Kong,Asia,Hong Kong,"Hong Kong, Tsim Sha Tsui",SASS 3100 electret filter air sampler,2017-07-05
+SRR10002772,SRR10002772,Hong Kong,Asia,Hong Kong,"Hong Kong, East Tsim Sha Tsui",SASS 3100 electret filter air sampler,2017-07-05
+SRR10002773,SRR10002773,Hong Kong,Asia,Hong Kong,"Hong Kong, Hung Hom",SASS 3100 electret filter air sampler,2017-07-05
+SRR10002774,SRR10002774,Hong Kong,Asia,Hong Kong,"Hong Kong, Mong Kok East",SASS 3100 electret filter air sampler,2017-07-05
diff --git a/data/2024-04-12_leung/1/taxonomic_composition.tsv b/data/2024-04-12_leung/1/taxonomic_composition.tsv
new file mode 100644
index 0000000..e07be97
--- /dev/null
+++ b/data/2024-04-12_leung/1/taxonomic_composition.tsv
@@ -0,0 +1,801 @@
+sample	classification	n_reads	p_reads
+SRR10002675	Filtered	416158	0.07219161122368457
+SRR10002675	Duplicate	287447	0.04986390282396219
+SRR10002675	Ribosomal	17673	0.003065764313448684
+SRR10002675	Unassigned	2696092	0.4676955038405754
+SRR10002675	Bacterial	736710	0.12779829272680246
+SRR10002675	Archaeal	7402	0.0012840370875429842
+SRR10002675	Viral	642	1.11368793596676e-4
+SRR10002675	Human	1602507	0.27798951919038706
+SRR10002676	Filtered	374568	0.06795372511113723
+SRR10002676	Duplicate	260153	0.04719667843712673
+SRR10002676	Ribosomal	15965	0.0028963531892721908
+SRR10002676	Unassigned	2673642	0.4850492661241515
+SRR10002676	Bacterial	753116	0.13662949755664988
+SRR10002676	Archaeal	785	1.4241385866449544e-4
+SRR10002676	Viral	811	1.4713075079860613e-4
+SRR10002676	Human	1433064	0.2599849349721994
+SRR10002677	Filtered	424418	0.072292846514011
+SRR10002677	Duplicate	296132	0.050441369649466106
+SRR10002677	Ribosomal	17548	0.0029890223096755205
+SRR10002677	Unassigned	3140286	0.534897704169233
+SRR10002677	Bacterial	911595	0.15527568910352496
+SRR10002677	Archaeal	1006	1.7135607724718334e-4
+SRR10002677	Viral	782	1.3320124493767136e-4
+SRR10002677	Human	1079049	0.18379881093190453
+SRR10002678	Filtered	324841	0.053517675820362126
+SRR10002678	Duplicate	518702	0.08545634782362287
+SRR10002678	Ribosomal	12907	0.002126433060523191
+SRR10002678	Unassigned	1840217	0.3031764366108937
+SRR10002678	Bacterial	1999342	0.3293923396678204
+SRR10002678	Archaeal	1339	2.2060074905404453e-4
+SRR10002678	Viral	1284	2.1153947855518537e-4
+SRR10002678	Human	1371157	0.22589862678916844
+SRR10002679	Filtered	506588	0.07346259634218372
+SRR10002679	Duplicate	635649	0.09217831038696679
+SRR10002679	Ribosomal	12461	0.001807025458597423
+SRR10002679	Unassigned	2642411	0.3831878620558442
+SRR10002679	Bacterial	1943505	0.2818363705891489
+SRR10002679	Archaeal	4868	7.059304977491578e-4
+SRR10002679	Viral	765	1.1093607863149254e-4
+SRR10002679	Human	1149616	0.16671096859087833
+SRR10002680	Filtered	241483	0.05523916927250726
+SRR10002680	Duplicate	386492	0.08840993780295041
+SRR10002680	Ribosomal	8916	0.002039532527066811
+SRR10002680	Unassigned	1220408	0.2791679915088103
+SRR10002680	Bacterial	909686	0.20809042019036553
+SRR10002680	Archaeal	993	2.2714847458247457e-4
+SRR10002680	Viral	658	1.5051731749775253e-4
+SRR10002680	Human	1602954	0.3666752829062195
+SRR10002681	Filtered	346899	0.06208871032839456
+SRR10002681	Duplicate	571543	0.10229596443697334
+SRR10002681	Ribosomal	27521	0.004925766280524726
+SRR10002681	Unassigned	2605853	0.46640103337103295
+SRR10002681	Bacterial	1095409	0.1960585994543552
+SRR10002681	Archaeal	5574	9.976462064476153e-4
+SRR10002681	Viral	727	1.3011998422809764e-4
+SRR10002681	Human	933625	0.16710215993804356
+SRR10002682	Filtered	300409	0.06953612645381473
+SRR10002682	Duplicate	410836	0.09509683147901503
+SRR10002682	Ribosomal	11174	0.0025864627124850643
+SRR10002682	Unassigned	1416455	0.3278689852705416
+SRR10002682	Bacterial	1179575	0.2730380127151933
+SRR10002682	Archaeal	1635	3.7845592759200644e-4
+SRR10002682	Viral	1046	2.421192050527454e-4
+SRR10002682	Human	999056	0.23125300623630557
+SRR10002683	Filtered	225620	0.06164438767494758
+SRR10002683	Duplicate	322471	0.08810622878259028
+SRR10002683	Ribosomal	8565	0.002340147949809086
+SRR10002683	Unassigned	1065064	0.2909991051973689
+SRR10002683	Bacterial	957056	0.26148892425598186
+SRR10002683	Archaeal	1051	2.8715650849379443e-4
+SRR10002683	Viral	961	2.6256651252382155e-4
+SRR10002683	Human	1079237	0.2948714831182847
+SRR10002684	Filtered	162792	0.049692186343867295
+SRR10002684	Duplicate	309110	0.09435569143909295
+SRR10002684	Ribosomal	9312	0.0028424839011382146
+SRR10002684	Unassigned	1141733	0.34851349569353923
+SRR10002684	Bacterial	839188	0.2561617676147311
+SRR10002684	Archaeal	696	2.1245369364177377e-4
+SRR10002684	Viral	826	2.5213613641969127e-4
+SRR10002684	Human	812351	0.24796978517756976
+SRR10002685	Filtered	282582	0.06665697963789698
+SRR10002685	Duplicate	399879	0.09432563418980192
+SRR10002685	Ribosomal	8483	0.002001016194479054
+SRR10002685	Unassigned	1059984	0.250034793102521
+SRR10002685	Bacterial	790824	0.1865438678513148
+SRR10002685	Archaeal	719	1.6960163194983376e-4
+SRR10002685	Viral	640	1.509666821250259e-4
+SRR10002685	Human	1696235	0.4001171407099114
+SRR10002686	Filtered	339980	0.07510838888551372
+SRR10002686	Duplicate	400667	0.08851536222599013
+SRR10002686	Ribosomal	11747	0.0025951474917293065
+SRR10002686	Unassigned	1410497	0.31160702746588165
+SRR10002686	Bacterial	1136035	0.250972876544369
+SRR10002686	Archaeal	2104	4.648157250871253e-4
+SRR10002686	Viral	825	1.822590176791247e-4
+SRR10002686	Human	1224670	0.2705541226437499
+SRR10002687	Filtered	449834	0.07262698994697565
+SRR10002687	Duplicate	600205	0.09690481933585393
+SRR10002687	Ribosomal	11737	0.001894972325363697
+SRR10002687	Unassigned	1202386	0.19412866954117355
+SRR10002687	Bacterial	1210981	0.19551635695162775
+SRR10002687	Archaeal	1123	1.813115720698161e-4
+SRR10002687	Viral	662	1.068817993857687e-4
+SRR10002687	Human	2716830	0.4386399985275498
+SRR10002688	Filtered	159982	0.13426309799959046
+SRR10002688	Duplicate	43753	0.03671921420394845
+SRR10002688	Ribosomal	10154	0.008521630540234786
+SRR10002688	Unassigned	110299	0.09256719784886316
+SRR10002688	Bacterial	854248	0.7169180466549621
+SRR10002688	Archaeal	13	1.0910104099177882e-5
+SRR10002688	Viral	175	1.4686678595047148e-4
+SRR10002688	Human	12932	0.010853035862351413
+SRR10002689	Filtered	599909	0.13453296636143233
+SRR10002689	Duplicate	211271	0.04737870966454274
+SRR10002689	Ribosomal	26182	0.005871460713666609
+SRR10002689	Unassigned	3001195	0.6730348535846252
+SRR10002689	Bacterial	566302	0.12699640764917988
+SRR10002689	Archaeal	1710	3.8347711482583077e-4
+SRR10002689	Viral	207	4.6420913899968986e-5
+SRR10002689	Human	52421	0.011755703997827412
+SRR10002690	Filtered	429228	0.08937474037311312
+SRR10002690	Duplicate	251210	0.05230746486512936
+SRR10002690	Ribosomal	20431	0.0042541850032222365
+SRR10002690	Unassigned	2029668	0.42262166154960945
+SRR10002690	Bacterial	800092	0.1665968081639707
+SRR10002690	Archaeal	1592	3.314895269507024e-4
+SRR10002690	Viral	510	1.0619325298043858e-4
+SRR10002690	Human	1269834	0.264407457265024
+SRR10002691	Filtered	628381	0.2539530827296175
+SRR10002691	Duplicate	109317	0.04417923066539821
+SRR10002691	Ribosomal	9916	0.004007439385256535
+SRR10002691	Unassigned	1362383	0.5505916994759937
+SRR10002691	Bacterial	301697	0.1219274344709299
+SRR10002691	Archaeal	420	1.6973825552720297e-4
+SRR10002691	Viral	158	6.385391517451922e-5
+SRR10002691	Human	62126	0.02510752110210241
+SRR10002692	Filtered	1483857	0.2899388744480812
+SRR10002692	Duplicate	232137	0.04535850860140447
+SRR10002692	Ribosomal	16213	0.003167946083367023
+SRR10002692	Unassigned	1890541	0.36940306891968017
+SRR10002692	Bacterial	710411	0.13881106180416025
+SRR10002692	Archaeal	452	8.83187337125698e-5
+SRR10002692	Viral	505	9.867469142665432e-5
+SRR10002692	Human	783711	0.1531335467181677
+SRR10002693	Filtered	700543	0.15459003826006518
+SRR10002693	Duplicate	238612	0.052654923693921246
+SRR10002693	Ribosomal	17365	0.0038319646536844015
+SRR10002693	Unassigned	1891746	0.4174548693204061
+SRR10002693	Bacterial	955493	0.21085029673728015
+SRR10002693	Archaeal	467	1.030536995836807e-4
+SRR10002693	Viral	1236	2.727502627096988e-4
+SRR10002693	Human	726156	0.16024210337234956
+SRR10002694	Filtered	643701	0.14051726076247686
+SRR10002694	Duplicate	206231	0.04501937266573513
+SRR10002694	Ribosomal	28334	0.006185194782117815
+SRR10002694	Unassigned	3094348	0.6754833452268192
+SRR10002694	Bacterial	513630	0.11212330048490059
+SRR10002694	Archaeal	1399	3.0539590245580656e-4
+SRR10002694	Viral	182	3.9729845780526655e-5
+SRR10002694	Human	93114	0.02032640032971406
+SRR10002695	Filtered	327947	0.09200553132382848
+SRR10002695	Duplicate	217599	0.061047399764394106
+SRR10002695	Ribosomal	11536	0.0032364248166675876
+SRR10002695	Unassigned	1945277	0.5457474651605995
+SRR10002695	Bacterial	402600	0.11294943058168956
+SRR10002695	Archaeal	803	2.252816511602005e-4
+SRR10002695	Viral	363	1.018396505244742e-4
+SRR10002695	Human	658302	0.18468662705113614
+SRR10002696	Filtered	504787	0.13672787256460747
+SRR10002696	Duplicate	223865	0.06063663523758705
+SRR10002696	Ribosomal	12076	0.003270935640359597
+SRR10002696	Unassigned	1867699	0.5058896343627012
+SRR10002696	Bacterial	441332	0.11954029215230057
+SRR10002696	Archaeal	739	2.0016739302962424e-4
+SRR10002696	Viral	364	9.859395272365795e-5
+SRR10002696	Human	641048	0.17363586869669087
+SRR10002697	Filtered	1524213	0.3304099893000276
+SRR10002697	Duplicate	295463	0.06404874297001406
+SRR10002697	Ribosomal	8821	0.0019121648454747094
+SRR10002697	Unassigned	720501	0.1561859974299256
+SRR10002697	Bacterial	698244	0.15136125500097983
+SRR10002697	Archaeal	290	6.286450574624938e-5
+SRR10002697	Viral	686	1.487070722135416e-4
+SRR10002697	Human	1364878	0.29587027887561845
+SRR10002698	Filtered	651509	0.15681758335287008
+SRR10002698	Duplicate	224068	0.05393294991582755
+SRR10002698	Ribosomal	12864	0.0030963523025028367
+SRR10002698	Unassigned	1358905	0.32708711331099327
+SRR10002698	Bacterial	696660	0.1676853851882483
+SRR10002698	Archaeal	416	1.0013079585208178e-4
+SRR10002698	Viral	779	1.8750454319416276e-4
+SRR10002698	Human	1209365	0.29109298059051175
+SRR10002699	Filtered	393779	0.08964481186561758
+SRR10002699	Duplicate	216493	0.049285193611703894
+SRR10002699	Ribosomal	21665	0.004932093506938168
+SRR10002699	Unassigned	2651654	0.6036559185805042
+SRR10002699	Bacterial	680700	0.1549631225558648
+SRR10002699	Archaeal	881	2.0056193766962964e-4
+SRR10002699	Viral	742	1.6891822673196957e-4
+SRR10002699	Human	426744	0.09714937971496984
+SRR10002700	Filtered	384742	0.07601049134519339
+SRR10002700	Duplicate	295215	0.058323336683988924
+SRR10002700	Ribosomal	17689	0.003494678463503142
+SRR10002700	Unassigned	2834060	0.5599032419173336
+SRR10002700	Bacterial	919345	0.18162785754023947
+SRR10002700	Archaeal	1044	2.062549785684482e-4
+SRR10002700	Viral	475	9.384206400384377e-5
+SRR10002700	Human	609126	0.12034029700716914
+SRR10002701	Filtered	308676	0.07063900661064995
+SRR10002701	Duplicate	252152	0.05770376315258914
+SRR10002701	Ribosomal	12185	0.002788478195748194
+SRR10002701	Unassigned	2117790	0.48464597769171675
+SRR10002701	Bacterial	894953	0.20480565668604298
+SRR10002701	Archaeal	929	2.1259714762823738e-4
+SRR10002701	Viral	836	1.9131454835006077e-4
+SRR10002701	Human	782246	0.17901320596727469
+SRR10002702	Filtered	548125	0.15268952234399621
+SRR10002702	Duplicate	193231	0.05382777485437215
+SRR10002702	Ribosomal	14429	0.004019442860481681
+SRR10002702	Unassigned	2295197	0.6393660818524481
+SRR10002702	Bacterial	440947	0.12283327125932607
+SRR10002702	Archaeal	781	2.1756080629539075e-4
+SRR10002702	Viral	142	3.955651023552559e-5
+SRR10002702	Human	96949	0.027006789512844864
+SRR10002703	Filtered	340611	0.06918870233324557
+SRR10002703	Duplicate	290098	0.05892793882014931
+SRR10002703	Ribosomal	13012	0.0026431424550592657
+SRR10002703	Unassigned	2076308	0.42176282082532995
+SRR10002703	Bacterial	814113	0.16537170561909498
+SRR10002703	Archaeal	898	1.8241176795598068e-4
+SRR10002703	Viral	463	9.404972000403012e-5
+SRR10002703	Human	1387425	0.28182922845916086
+SRR10002704	Filtered	361793	0.09093315102785604
+SRR10002704	Duplicate	244627	0.06148461671865221
+SRR10002704	Ribosomal	11081	0.0027851015540369018
+SRR10002704	Unassigned	1983736	0.4985927458170695
+SRR10002704	Bacterial	494174	0.1242058275755466
+SRR10002704	Archaeal	695	1.7468148904030743e-4
+SRR10002704	Viral	231	5.805960283210218e-5
+SRR10002704	Human	882333	0.2217658162149663
+SRR10002705	Filtered	477607	0.12983153952233242
+SRR10002705	Duplicate	189186	0.05142786775753282
+SRR10002705	Ribosomal	12733	0.003461308131450876
+SRR10002705	Unassigned	1710685	0.46502850081293035
+SRR10002705	Bacterial	689104	0.18732437592203915
+SRR10002705	Archaeal	1696	4.6103656569077873e-4
+SRR10002705	Viral	246	6.687204903297852e-5
+SRR10002705	Human	597410	0.16239849923899063
+SRR10002706	Filtered	535194	0.13630792897238153
+SRR10002706	Duplicate	192905	0.04913074705325034
+SRR10002706	Ribosomal	13741	0.0034996790920852902
+SRR10002706	Unassigned	1958947	0.49892190221986776
+SRR10002706	Bacterial	738085	0.1879819985941177
+SRR10002706	Archaeal	1331	3.38990821014884e-4
+SRR10002706	Viral	267	6.800191525993542e-5
+SRR10002706	Human	485890	0.12375075133202254
+SRR10002707	Filtered	444979	0.1496137094317571
+SRR10002707	Duplicate	153172	0.05150047777778525
+SRR10002707	Ribosomal	11360	0.0038195324703969424
+SRR10002707	Unassigned	1555603	0.5230348740798323
+SRR10002707	Bacterial	491623	0.165296655958975
+SRR10002707	Archaeal	1337	4.4953476346133026e-4
+SRR10002707	Viral	154	5.177887327826841e-5
+SRR10002707	Human	315958	0.10623343664451383
+SRR10002708	Filtered	545969	0.12120521229765517
+SRR10002708	Duplicate	259964	0.057712052899977155
+SRR10002708	Ribosomal	15621	0.003467864697998735
+SRR10002708	Unassigned	2476226	0.5497225996841826
+SRR10002708	Bacterial	757588	0.1681846668476708
+SRR10002708	Archaeal	1353	3.003662336849298e-4
+SRR10002708	Viral	261	5.794204507891107e-5
+SRR10002708	Human	447519	0.09934929529375174
+SRR10002709	Filtered	695024	0.1812239206271852
+SRR10002709	Duplicate	168701	0.04398791499822563
+SRR10002709	Ribosomal	15955	0.004160183898119691
+SRR10002709	Unassigned	2374068	0.6190259772260244
+SRR10002709	Bacterial	548204	0.14294136343997538
+SRR10002709	Archaeal	1010	2.633522868756432e-4
+SRR10002709	Viral	167	4.354438802795289e-5
+SRR10002709	Human	32038	0.008353743135566196
+SRR10002710	Filtered	420704	0.12400486818381314
+SRR10002710	Duplicate	172755	0.050920507062197265
+SRR10002710	Ribosomal	11874	0.00349992822700663
+SRR10002710	Unassigned	1903059	0.5609373346605196
+SRR10002710	Bacterial	596559	0.1758391176667381
+SRR10002710	Archaeal	3277	9.659141653950417e-4
+SRR10002710	Viral	283	8.341584034385012e-5
+SRR10002710	Human	284130	0.08374891419398633
+SRR10002711	Filtered	382448	0.17199032226149683
+SRR10002711	Duplicate	111861	0.05030490272793502
+SRR10002711	Ribosomal	6812	0.0030634179685743324
+SRR10002711	Unassigned	926704	0.41674716458451383
+SRR10002711	Bacterial	423945	0.19065189822185047
+SRR10002711	Archaeal	1018	4.578038009407913e-4
+SRR10002711	Viral	170	7.645053650288264e-5
+SRR10002711	Human	370702	0.16670803989818586
+SRR10002712	Filtered	487784	0.16104955721474837
+SRR10002712	Duplicate	174534	0.05762514436496255
+SRR10002712	Ribosomal	6750	0.002228618632836566
+SRR10002712	Unassigned	1420666	0.46905521757591007
+SRR10002712	Bacterial	553273	0.18267178027339043
+SRR10002712	Archaeal	1638	5.408114549016734e-4
+SRR10002712	Viral	212	6.999513335723733e-5
+SRR10002712	Human	383925	0.12675887534989314
+SRR10002713	Filtered	280363	0.07521067939443657
+SRR10002713	Duplicate	217141	0.05825063269542469
+SRR10002713	Ribosomal	11303	0.0030321629786930395
+SRR10002713	Unassigned	2017160	0.5411269463063303
+SRR10002713	Bacterial	713148	0.19131035689011622
+SRR10002713	Archaeal	3877	0.001040050948278591
+SRR10002713	Viral	367	9.845207583653414e-5
+SRR10002713	Human	484343	0.12993071871088407
+SRR10002714	Filtered	498998	0.0801438032932329
+SRR10002714	Duplicate	346492	0.05564989577248577
+SRR10002714	Ribosomal	15231	0.002446242806502692
+SRR10002714	Unassigned	2892877	0.4646234358444677
+SRR10002714	Bacterial	1260539	0.20245449813315586
+SRR10002714	Archaeal	2954	4.744403683546026e-4
+SRR10002714	Viral	896	1.439060832923913e-4
+SRR10002714	Human	1208296	0.19406377769850808
+SRR10002715	Filtered	437335	0.06659467160399304
+SRR10002715	Duplicate	405126	0.06169008409626325
+SRR10002715	Ribosomal	16510	0.0025140407883703
+SRR10002715	Unassigned	3010995	0.45849571432943864
+SRR10002715	Bacterial	1145462	0.17442387580425323
+SRR10002715	Archaeal	1515	2.3069483915087854e-4
+SRR10002715	Viral	761	1.1588037794971522e-4
+SRR10002715	Human	1549413	0.23593503816058098
+SRR10002716	Filtered	347743	0.05851385775705769
+SRR10002716	Duplicate	369782	0.06222230598206235
+SRR10002716	Ribosomal	19177	0.003226866537089446
+SRR10002716	Unassigned	3075922	0.5175778157426731
+SRR10002716	Bacterial	1055653	0.17763212913793008
+SRR10002716	Archaeal	1211	2.0377198604658284e-4
+SRR10002716	Viral	640	1.0769122301388359e-4
+SRR10002716	Human	1072789	0.1805155616341268
+SRR10002717	Filtered	318050	0.07346548265701817
+SRR10002717	Duplicate	212495	0.04908362753404521
+SRR10002717	Ribosomal	16105	0.003720049043204772
+SRR10002717	Unassigned	2440623	0.56375270139544
+SRR10002717	Bacterial	944083	0.21807109971163557
+SRR10002717	Archaeal	1137	2.6263245961650577e-4
+SRR10002717	Viral	355	8.200046012652556e-5
+SRR10002717	Human	396396	0.0915624067389133
+SRR10002718	Filtered	350261	0.07735019047093247
+SRR10002718	Duplicate	250823	0.055390713851929556
+SRR10002718	Ribosomal	10830	0.0023916524043504665
+SRR10002718	Unassigned	2162421	0.47754010931375257
+SRR10002718	Bacterial	879421	0.19420769612985148
+SRR10002718	Archaeal	4212	9.30160658090874e-4
+SRR10002718	Viral	577	1.27422293380445e-4
+SRR10002718	Human	869705	0.19206205487771214
+SRR10002719	Filtered	254946	0.07135885685208318
+SRR10002719	Duplicate	186570	0.052220556207562226
+SRR10002719	Ribosomal	14293	0.0040005810680960865
+SRR10002719	Unassigned	1942360	0.5436625371459536
+SRR10002719	Bacterial	743595	0.20813069889672633
+SRR10002719	Archaeal	931	2.6058496987318666e-4
+SRR10002719	Viral	307	8.592866353498207e-5
+SRR10002719	Human	429729	0.12028025619617037
+SRR10002720	Filtered	465297	0.08675327009545344
+SRR10002720	Duplicate	348380	0.06495443606095477
+SRR10002720	Ribosomal	16572	0.0030898011206215697
+SRR10002720	Unassigned	2830787	0.5277919891890521
+SRR10002720	Bacterial	799184	0.14900552852901452
+SRR10002720	Archaeal	835	1.5568331738589252e-4
+SRR10002720	Viral	370	6.898542207518591e-5
+SRR10002720	Human	902027	0.16818030626544248
+SRR10002721	Filtered	456021	0.08943006841673512
+SRR10002721	Duplicate	306485	0.06010463228382698
+SRR10002721	Ribosomal	19080	0.00374177001802835
+SRR10002721	Unassigned	2830148	0.555019021644806
+SRR10002721	Bacterial	1087562	0.2132812832466954
+SRR10002721	Archaeal	1513	2.967137336099001e-4
+SRR10002721	Viral	360	7.05994343024217e-5
+SRR10002721	Human	398022	0.0780559112219958
+SRR10002722	Filtered	378250	0.07983073562466364
+SRR10002722	Duplicate	225835	0.047663117461456475
+SRR10002722	Ribosomal	14878	0.0031400441100429494
+SRR10002722	Unassigned	2625478	0.5541145805852495
+SRR10002722	Bacterial	913143	0.19272142080769922
+SRR10002722	Archaeal	1720	3.6301087977375134e-4
+SRR10002722	Viral	399	8.42100819940272e-5
+SRR10002722	Human	578447	0.12208288044912044
+SRR10002723	Filtered	437569	0.11774502306362179
+SRR10002723	Duplicate	217354	0.05848757965708369
+SRR10002723	Ribosomal	16059	0.004321300927119386
+SRR10002723	Unassigned	2196688	0.5911046697174188
+SRR10002723	Bacterial	460580	0.12393703101143574
+SRR10002723	Archaeal	2813	7.569474754335159e-4
+SRR10002723	Viral	241	6.485045914663254e-5
+SRR10002723	Human	384938	0.1035825976887404
+SRR10002724	Filtered	368965	0.06855887285210598
+SRR10002724	Duplicate	481425	0.08945551844436496
+SRR10002724	Ribosomal	14078	0.0026158898866069894
+SRR10002724	Unassigned	1573548	0.292387292178623
+SRR10002724	Bacterial	1619945	0.30100850563713305
+SRR10002724	Archaeal	1945	3.614082845184397e-4
+SRR10002724	Viral	1044	1.9398984526336816e-4
+SRR10002724	Human	1320775	0.2454185228713842
+SRR10002725	Filtered	426227	0.07611574288866484
+SRR10002725	Duplicate	535631	0.09565314135237428
+SRR10002725	Ribosomal	10931	0.001952061191609155
+SRR10002725	Unassigned	2139243	0.3820266434655149
+SRR10002725	Bacterial	1352230	0.2414816306952381
+SRR10002725	Archaeal	2953	5.27347607613378e-4
+SRR10002725	Viral	602	1.0750533687208044e-4
+SRR10002725	Human	1131905	0.20213592746211329
+SRR10002726	Filtered	207528	0.06327868012689399
+SRR10002726	Duplicate	327545	0.09987382561468088
+SRR10002726	Ribosomal	10385	0.0031665562869482387
+SRR10002726	Unassigned	1586605	0.4837818042998084
+SRR10002726	Bacterial	697718	0.21274562536513733
+SRR10002726	Archaeal	2010	6.128818619899817e-4
+SRR10002726	Viral	323	9.848798080734531e-5
+SRR10002726	Human	447474	0.13644213846373385
+SRR10002727	Filtered	291998	0.06405569197951433
+SRR10002727	Duplicate	403089	0.08842575916386568
+SRR10002727	Ribosomal	10924	0.0023964012739272683
+SRR10002727	Unassigned	1123673	0.2465004951187912
+SRR10002727	Bacterial	1179170	0.25867488925089865
+SRR10002727	Archaeal	1809	3.9684089202988174e-4
+SRR10002727	Viral	696	1.5268173623703577e-4
+SRR10002727	Human	1547143	0.33939724058473597
+SRR10002728	Filtered	419722	0.07103260457509261
+SRR10002728	Duplicate	536630	0.09081779509563936
+SRR10002728	Ribosomal	17363	0.0029384666832744837
+SRR10002728	Unassigned	1835525	0.31063923623897927
+SRR10002728	Bacterial	1794132	0.3036339980070619
+SRR10002728	Archaeal	1966	3.327204687736932e-4
+SRR10002728	Viral	1174	1.9868455256374153e-4
+SRR10002728	Human	1302352	0.2204064943786149
+SRR10002729	Filtered	235486	0.05299765108548118
+SRR10002729	Duplicate	447043	0.10060992557607146
+SRR10002729	Ribosomal	11223	0.002525808914892415
+SRR10002729	Unassigned	1500844	0.3377746729985558
+SRR10002729	Bacterial	1174115	0.2642421931844345
+SRR10002729	Archaeal	928	2.0885241673529014e-4
+SRR10002729	Viral	849	1.9107295453476436e-4
+SRR10002729	Human	1072841	0.24144982286929462
+SRR10002730	Filtered	327503	0.06305212216097185
+SRR10002730	Duplicate	503820	0.09699734105379443
+SRR10002730	Ribosomal	12906	0.002484712166329782
+SRR10002730	Unassigned	1616778	0.31126824475858766
+SRR10002730	Bacterial	1173157	0.22586064395745764
+SRR10002730	Archaeal	1094	2.1062103749920825e-4
+SRR10002730	Viral	1187	2.2852575092464369e-4
+SRR10002730	Human	1557718	0.2998977891144348
+SRR10002731	Filtered	411257	0.07107422476838839
+SRR10002731	Duplicate	522396	0.09028148024740495
+SRR10002731	Ribosomal	17963	0.0031044001670842334
+SRR10002731	Unassigned	2942831	0.5085857066247654
+SRR10002731	Bacterial	1247493	0.21559413670525032
+SRR10002731	Archaeal	2331	4.028478978719227e-4
+SRR10002731	Viral	424	7.327649450780576e-5
+SRR10002731	Human	641608	0.11088392709472698
+SRR10002732	Filtered	358858	0.06816789081831179
+SRR10002732	Duplicate	470287	0.08933470305600374
+SRR10002732	Ribosomal	12791	0.0024297507411205157
+SRR10002732	Unassigned	1633198	0.3102387656083609
+SRR10002732	Bacterial	1397846	0.2655318078705612
+SRR10002732	Archaeal	2078	3.94732393092677e-4
+SRR10002732	Viral	1390	2.640413986519832e-4
+SRR10002732	Human	1387878	0.2636383081138972
+SRR10002733	Filtered	427820	0.0811492072199312
+SRR10002733	Duplicate	491323	0.09319450221803155
+SRR10002733	Ribosomal	11279	0.0021394088827862276
+SRR10002733	Unassigned	1675356	0.317782738560972
+SRR10002733	Bacterial	1440395	0.2732151660360731
+SRR10002733	Archaeal	1404	2.663117360964504e-4
+SRR10002733	Viral	884	1.676777597644317e-4
+SRR10002733	Human	1223556	0.23208498758634505
+SRR10002734	Filtered	296175	0.061768367838529445
+SRR10002734	Duplicate	492109	0.10263111244585427
+SRR10002734	Ribosomal	9584	0.001998777875797978
+SRR10002734	Unassigned	2143894	0.44711685050668104
+SRR10002734	Bacterial	1184687	0.2470707601570826
+SRR10002734	Archaeal	6058	0.0012634178184040227
+SRR10002734	Viral	903	1.8832391713747646e-4
+SRR10002734	Human	661520	0.1379623894405132
+SRR10002735	Filtered	309462	0.06397132740369996
+SRR10002735	Duplicate	435139	0.08995100993052005
+SRR10002735	Ribosomal	11888	0.002457462112230856
+SRR10002735	Unassigned	1309685	0.2707353016871693
+SRR10002735	Bacterial	1433050	0.2962370524842217
+SRR10002735	Archaeal	1327	2.743146217135217e-4
+SRR10002735	Viral	1040	2.1498659124496048e-4
+SRR10002735	Human	1335920	0.27615854516919963
+SRR10002736	Filtered	317364	0.09135021959353463
+SRR10002736	Duplicate	206794	0.059523693016931355
+SRR10002736	Ribosomal	10094	0.002905462234459922
+SRR10002736	Unassigned	1624681	0.46764902799133945
+SRR10002736	Bacterial	433816	0.12486982412368391
+SRR10002736	Archaeal	1267	3.6469394205079466e-4
+SRR10002736	Viral	351	1.0103202340949402e-4
+SRR10002736	Human	879779	0.2532360470745904
+SRR10002737	Filtered	548466	0.08965721521321432
+SRR10002737	Duplicate	396457	0.06480844860353296
+SRR10002737	Ribosomal	13311	0.0021759365060060164
+SRR10002737	Unassigned	3257266	0.5324621740794976
+SRR10002737	Bacterial	1548417	0.2531182538367003
+SRR10002737	Archaeal	3820	6.244517656782347e-4
+SRR10002737	Viral	421	6.882046946349131e-5
+SRR10002737	Human	349208	0.057084699525907064
+SRR10002738	Filtered	458654	0.12145103393343805
+SRR10002738	Duplicate	204551	0.054164861621437264
+SRR10002738	Ribosomal	11035	0.0029220548811424055
+SRR10002738	Unassigned	1705265	0.4515521447114911
+SRR10002738	Bacterial	615187	0.1629007862406301
+SRR10002738	Archaeal	2064	5.465447462327073e-4
+SRR10002738	Viral	283	7.493806355806985e-5
+SRR10002738	Human	779413	0.2063876358020703
+SRR10002739	Filtered	705090	0.15567673976357377
+SRR10002739	Duplicate	270251	0.059668687114901045
+SRR10002739	Ribosomal	13356	0.002948869699304048
+SRR10002739	Unassigned	2160897	0.477104199357369
+SRR10002739	Bacterial	626167	0.13825133969782255
+SRR10002739	Archaeal	1005	2.218938340671285e-4
+SRR10002739	Viral	403	8.897832351149532e-5
+SRR10002739	Human	752024	0.166039292209451
+SRR10002740	Filtered	356790	0.0926827418235321
+SRR10002740	Duplicate	252479	0.06558604773918429
+SRR10002740	Ribosomal	11878	0.0030855282025278575
+SRR10002740	Unassigned	1785842	0.4639051907946417
+SRR10002740	Bacterial	639440	0.166106259793266
+SRR10002740	Archaeal	1887	4.901828353401302e-4
+SRR10002740	Viral	1034	2.686004513734471e-4
+SRR10002740	Human	800234	0.2078754483601345
+SRR10002741	Filtered	266006	0.09372236397703643
+SRR10002741	Duplicate	168546	0.059384109978247036
+SRR10002741	Ribosomal	8877	0.003127649094472126
+SRR10002741	Unassigned	1520095	0.5355777571546251
+SRR10002741	Bacterial	466105	0.16422359819521576
+SRR10002741	Archaeal	3382	0.0011915860355418194
+SRR10002741	Viral	210	7.398967104192255e-5
+SRR10002741	Human	405013	0.1426989458938199
+SRR10002742	Filtered	476284	0.13575854177373367
+SRR10002742	Duplicate	174675	0.04978883037080173
+SRR10002742	Ribosomal	10229	0.0029156430276967562
+SRR10002742	Unassigned	1929036	0.5498465503544863
+SRR10002742	Bacterial	556236	0.15854781651715052
+SRR10002742	Archaeal	4445	0.0012669892714939955
+SRR10002742	Viral	231	6.584353694378245e-5
+SRR10002742	Human	357181	0.10180978514769333
+SRR10002743	Filtered	427623	0.0774600203892211
+SRR10002743	Duplicate	277697	0.05030228795463652
+SRR10002743	Ribosomal	9163	0.0016597941804496787
+SRR10002743	Unassigned	1982908	0.3591857643530625
+SRR10002743	Bacterial	975239	0.1766556822817379
+SRR10002743	Archaeal	1208	2.1881822219613792e-4
+SRR10002743	Viral	291	5.271200551248025e-5
+SRR10002743	Human	1846435	0.3344649206131837
+SRR10002744	Filtered	546275	0.14597634129082876
+SRR10002744	Duplicate	192270	0.05137864837304955
+SRR10002744	Ribosomal	18130	0.004844723019729487
+SRR10002744	Unassigned	2383832	0.6370107978801866
+SRR10002744	Bacterial	391045	0.10449557160783877
+SRR10002744	Archaeal	435	1.1624128591187682e-4
+SRR10002744	Viral	423	1.1303462974879055e-4
+SRR10002744	Human	209806	0.05606464191270627
+SRR10002745	Filtered	286653	0.08429168233733551
+SRR10002745	Duplicate	207228	0.06093638213240875
+SRR10002745	Ribosomal	10559	0.0031049243294154457
+SRR10002745	Unassigned	1663402	0.48913129457318977
+SRR10002745	Bacterial	483155	0.14207403299353344
+SRR10002745	Archaeal	1017	2.9905370234070537e-4
+SRR10002745	Viral	298	8.762832182648004e-5
+SRR10002745	Human	748415	0.22007500160994986
+SRR10002746	Filtered	997779	0.2510736530928532
+SRR10002746	Duplicate	230192	0.057923795101670866
+SRR10002746	Ribosomal	14234	0.003581737416926666
+SRR10002746	Unassigned	2146482	0.5401246939833907
+SRR10002746	Bacterial	304384	0.07659291568875976
+SRR10002746	Archaeal	393	9.889158387327384e-5
+SRR10002746	Viral	464	1.1675749342798743e-4
+SRR10002746	Human	280121	0.07048755563909756
+SRR10002747	Filtered	554002	0.12873983214617357
+SRR10002747	Duplicate	221848	0.051553377572579724
+SRR10002747	Ribosomal	13585	0.003156903079241172
+SRR10002747	Unassigned	2272258	0.5280307896231422
+SRR10002747	Bacterial	610882	0.14195769354825216
+SRR10002747	Archaeal	874	2.0310145684628519e-4
+SRR10002747	Viral	324	7.529161558146042e-5
+SRR10002747	Human	629495	0.1462830109581834
+SRR10002748	Filtered	793430	0.17086512975697324
+SRR10002748	Duplicate	261688	0.05635450395856322
+SRR10002748	Ribosomal	12334	0.0026561265775462335
+SRR10002748	Unassigned	1752597	0.3774217181310034
+SRR10002748	Bacterial	721922	0.1554658838264417
+SRR10002748	Archaeal	704	1.5160638159498528e-4
+SRR10002748	Viral	601	1.2942533428776442e-4
+SRR10002748	Human	1100328	0.23695560603358942
+SRR10002749	Filtered	622582	0.138474859725093
+SRR10002749	Duplicate	252885	0.05624675127385652
+SRR10002749	Ribosomal	12074	0.002685502401805341
+SRR10002749	Unassigned	2002236	0.44533788197623975
+SRR10002749	Bacterial	722780	0.16076092645162038
+SRR10002749	Archaeal	1182	2.629007651924725e-4
+SRR10002749	Viral	529	1.1766032553876307e-4
+SRR10002749	Human	881725	0.1961135170806538
+SRR10002750	Filtered	443324	0.14529310879940902
+SRR10002750	Duplicate	164072	0.05377225448416201
+SRR10002750	Ribosomal	11771	0.0038577771193931383
+SRR10002750	Unassigned	1733779	0.5682213028871222
+SRR10002750	Bacterial	448480	0.14698291415388962
+SRR10002750	Archaeal	2498	8.186838199170895e-4
+SRR10002750	Viral	210	6.882450047341425e-5
+SRR10002750	Human	247105	0.08098513423563346
+SRR10002751	Filtered	783507	0.16034560667195485
+SRR10002751	Duplicate	257993	0.05279856351266504
+SRR10002751	Ribosomal	17286	0.003537599736736764
+SRR10002751	Unassigned	2359808	0.4829374152232621
+SRR10002751	Bacterial	751317	0.15375788623197126
+SRR10002751	Archaeal	1519	3.108650931449233e-4
+SRR10002751	Viral	775	1.5860463935965475e-4
+SRR10002751	Human	714159	0.1461534588909054
+SRR10002752	Filtered	444157	0.09893484391751305
+SRR10002752	Duplicate	266390	0.05933769606509928
+SRR10002752	Ribosomal	11689	0.0026036950685271423
+SRR10002752	Unassigned	2021604	0.45030715761098
+SRR10002752	Bacterial	797220	0.1775787306468653
+SRR10002752	Archaeal	3386	7.542229020474723e-4
+SRR10002752	Viral	450	1.0023635733058552e-4
+SRR10002752	Human	944493	0.21038341743163713
+SRR10002753	Filtered	755186	0.09941690221352835
+SRR10002753	Duplicate	486678	0.06406900966844664
+SRR10002753	Ribosomal	22191	0.002921347160858924
+SRR10002753	Unassigned	3420182	0.45025185774957405
+SRR10002753	Bacterial	1239333	0.16315271690815075
+SRR10002753	Archaeal	4396	5.787139885149759e-4
+SRR10002753	Viral	1262	1.6613672736712912e-4
+SRR10002753	Human	1666925	0.2194433155835592
+SRR10002754	Filtered	590984	0.1206077981276918
+SRR10002754	Duplicate	271505	0.055408640894946334
+SRR10002754	Ribosomal	14306	0.0029195632369315564
+SRR10002754	Unassigned	1955740	0.3991267024323027
+SRR10002754	Bacterial	721967	0.14733876076315988
+SRR10002754	Archaeal	1463	2.985685038187381e-4
+SRR10002754	Viral	504	1.0285613528683801e-4
+SRR10002754	Human	1343579	0.27419710990586216
+SRR10002755	Filtered	406652	0.09502080797081977
+SRR10002755	Duplicate	213513	0.0498907610740231
+SRR10002755	Ribosomal	15903	0.00371599281242917
+SRR10002755	Unassigned	2239275	0.5232427721217587
+SRR10002755	Bacterial	944796	0.22076684557705026
+SRR10002755	Archaeal	1151	2.6894974074740454e-4
+SRR10002755	Viral	376	8.78584730851643e-5
+SRR10002755	Human	457944	0.10700601223008639
+SRR10002756	Filtered	301404	0.06805109137690774
+SRR10002756	Duplicate	260294	0.05876926244794635
+SRR10002756	Ribosomal	12475	0.002816609484037783
+SRR10002756	Unassigned	2211703	0.499359009673332
+SRR10002756	Bacterial	898960	0.2029674758934353
+SRR10002756	Archaeal	1060	2.393271385234509e-4
+SRR10002756	Viral	519	1.1717998574874624e-4
+SRR10002756	Human	742669	0.16768004400006864
+SRR10002757	Filtered	324496	0.0721417722239415
+SRR10002757	Duplicate	283059	0.06292952117726153
+SRR10002757	Ribosomal	16032	0.003564225421250894
+SRR10002757	Unassigned	2535315	0.5636498361950293
+SRR10002757	Bacterial	854154	0.18989504743407784
+SRR10002757	Archaeal	5570	0.0012383193361007658
+SRR10002757	Viral	330	7.336541847634699e-5
+SRR10002757	Human	479076	0.10650791279386185
+SRR10002758	Filtered	282708	0.0765140461685735
+SRR10002758	Duplicate	198981	0.05385359247233515
+SRR10002758	Ribosomal	10677	0.0028896970405572512
+SRR10002758	Unassigned	2077707	0.5623249760274501
+SRR10002758	Bacterial	686836	0.18589003995019016
+SRR10002758	Archaeal	983	2.660459109176527e-4
+SRR10002758	Viral	369	9.986870918475468e-5
+SRR10002758	Human	436590	0.11816173372079145
+SRR10002759	Filtered	388323	0.07982557408106328
+SRR10002759	Duplicate	244837	0.050329890532585735
+SRR10002759	Ribosomal	18819	0.0038685256310636504
+SRR10002759	Unassigned	3148637	0.6472492128920431
+SRR10002759	Bacterial	799568	0.16436310652948088
+SRR10002759	Archaeal	986	2.0268697976665918e-4
+SRR10002759	Viral	330	6.783641310648837e-5
+SRR10002759	Human	263144	0.054093166940890225
+SRR10002760	Filtered	303623	0.08522198279915043
+SRR10002760	Duplicate	206180	0.05787133522008819
+SRR10002760	Ribosomal	10142	0.0028466926074407527
+SRR10002760	Unassigned	2085863	0.5854674405673626
+SRR10002760	Bacterial	645370	0.1811447454214197
+SRR10002760	Archaeal	3326	9.335535015133053e-4
+SRR10002760	Viral	375	1.0525633285252241e-4
+SRR10002760	Human	307852	0.0864089935501726
+SRR10002761	Filtered	311465	0.0713299153723737
+SRR10002761	Duplicate	277631	0.0635814481073234
+SRR10002761	Ribosomal	11971	0.002741529279125056
+SRR10002761	Unassigned	2040250	0.4672462711331464
+SRR10002761	Bacterial	791385	0.1812384218996226
+SRR10002761	Archaeal	1668	3.8199572613654607e-4
+SRR10002761	Viral	356	8.15290638516849e-5
+SRR10002761	Human	931815	0.21339888941842067
+SRR10002762	Filtered	385868	0.0817836806513734
+SRR10002762	Duplicate	313857	0.06652114365067355
+SRR10002762	Ribosomal	17627	0.0037359950523022352
+SRR10002762	Unassigned	2587396	0.5483915955265555
+SRR10002762	Bacterial	851926	0.18056341526792047
+SRR10002762	Archaeal	2396	5.078257301478502e-4
+SRR10002762	Viral	401	8.499086719085473e-5
+SRR10002762	Human	558683	0.11841135325383614
+SRR10002763	Filtered	276976	0.08285659242631108
+SRR10002763	Duplicate	161871	0.04842325498468965
+SRR10002763	Ribosomal	11149	0.003335192034547911
+SRR10002763	Unassigned	1738032	0.519927390993755
+SRR10002763	Bacterial	622256	0.1861461345994838
+SRR10002763	Archaeal	932	2.7880518218662237e-4
+SRR10002763	Viral	408	1.2205205400444414e-4
+SRR10002763	Human	531212	0.1589105777250215
+SRR10002764	Filtered	452427	0.08035837982734281
+SRR10002764	Duplicate	314471	0.05585515467176875
+SRR10002764	Ribosomal	21213	0.0037677731684391583
+SRR10002764	Unassigned	3159574	0.5611916344174791
+SRR10002764	Bacterial	1137818	0.20209494795489116
+SRR10002764	Archaeal	2729	4.847147021482328e-4
+SRR10002764	Viral	552	9.804416107945201e-5
+SRR10002764	Human	541332	0.09614935109685128
+SRR10002765	Filtered	252457	0.05315553410724129
+SRR10002765	Duplicate	451875	0.09514355702044174
+SRR10002765	Ribosomal	10813	0.0022767076781455855
+SRR10002765	Unassigned	1303511	0.27445792122881996
+SRR10002765	Bacterial	1000950	0.21075284846386977
+SRR10002765	Archaeal	1012	2.1307945716113314e-4
+SRR10002765	Viral	780	1.6423120215976664e-4
+SRR10002765	Human	1728004	0.36383612084216077
+SRR10002766	Filtered	263413	0.057221898786972994
+SRR10002766	Duplicate	419430	0.09111388203399257
+SRR10002766	Ribosomal	9147	0.001987026867331688
+SRR10002766	Unassigned	1125780	0.2445561502902228
+SRR10002766	Bacterial	1086177	0.23595308644120816
+SRR10002766	Archaeal	921	2.000712523026659e-4
+SRR10002766	Viral	782	1.6987591672169893e-4
+SRR10002766	Human	1697710	0.36879800841124744
+SRR10002767	Filtered	378056	0.07128723027531274
+SRR10002767	Duplicate	520504	0.09814759852302671
+SRR10002767	Ribosomal	24337	0.004589048509242774
+SRR10002767	Unassigned	2887437	0.5444626889256041
+SRR10002767	Bacterial	997924	0.18817116507940937
+SRR10002767	Archaeal	773	1.4575890609543757e-4
+SRR10002767	Viral	954	1.798887405110575e-4
+SRR10002767	Human	493293	0.09301662104079779
+SRR10002768	Filtered	296444	0.06300947148553813
+SRR10002768	Duplicate	403852	0.08583915032308816
+SRR10002768	Ribosomal	8886	0.001888728271175979
+SRR10002768	Unassigned	1107756	0.23545465617429864
+SRR10002768	Bacterial	633469	0.1346444754910619
+SRR10002768	Archaeal	426	9.054673008338589e-5
+SRR10002768	Viral	404	8.587060787250681e-5
+SRR10002768	Human	2253516	0.4789871009168813
+SRR10002769	Filtered	186340	0.057426188671872234
+SRR10002769	Duplicate	274868	0.08470871325458934
+SRR10002769	Ribosomal	3884	0.001196969608251324
+SRR10002769	Unassigned	450698	0.138895934217213
+SRR10002769	Bacterial	305962	0.09429125007203698
+SRR10002769	Archaeal	217	6.687497553824339e-5
+SRR10002769	Viral	196	6.040320371196177e-5
+SRR10002769	Human	2022696	0.6233536659967869
+SRR10002770	Filtered	241902	0.047577295256077415
+SRR10002770	Duplicate	533023	0.10483498544567697
+SRR10002770	Ribosomal	7404	0.0014562190228935568
+SRR10002770	Unassigned	727159	0.14301766186767367
+SRR10002770	Bacterial	533484	0.10492565494453623
+SRR10002770	Archaeal	296	5.821729210919676e-5
+SRR10002770	Viral	268	5.27102509637322e-5
+SRR10002770	Human	3040864	0.5980772559200692
+SRR10002771	Filtered	336581	0.059911722231725494
+SRR10002771	Duplicate	525437	0.09352826093650904
+SRR10002771	Ribosomal	12235	0.002177841059076898
+SRR10002771	Unassigned	1291404	0.22987107928534062
+SRR10002771	Bacterial	1287080	0.22910140337692636
+SRR10002771	Archaeal	892	1.5877680626862222e-4
+SRR10002771	Viral	2336	4.1581011148374614e-4
+SRR10002771	Human	2161984	0.38483510619266925
+SRR10002772	Filtered	425667	0.07019251587863949
+SRR10002772	Duplicate	591014	0.0974582468913452
+SRR10002772	Ribosomal	12275	0.0020241482952878653
+SRR10002772	Unassigned	1693952	0.279332794549855
+SRR10002772	Bacterial	1379812	0.22753108819696455
+SRR10002772	Archaeal	1463	2.4124879478665148e-4
+SRR10002772	Viral	901	1.4857495837510115e-4
+SRR10002772	Human	1959195	0.32307138243474615
+SRR10002773	Filtered	323993	0.057220803249081534
+SRR10002773	Duplicate	531750	0.09391302320636281
+SRR10002773	Ribosomal	14936	0.002637865377734339
+SRR10002773	Unassigned	2128249	0.3758726802556059
+SRR10002773	Bacterial	1146796	0.20253705568587502
+SRR10002773	Archaeal	1233	2.1776165042490896e-4
+SRR10002773	Viral	1406	2.483153937529781e-4
+SRR10002773	Human	1513791	0.2673524951811625
+SRR10002774	Filtered	337651	0.05724844752300528
+SRR10002774	Duplicate	564630	0.09573254906668267
+SRR10002774	Ribosomal	14818	0.0025123796328039667
+SRR10002774	Unassigned	2982699	0.5057141461995384
+SRR10002774	Bacterial	1078791	0.18290812096451778
+SRR10002774	Archaeal	1279	2.1685339116994694e-4
+SRR10002774	Viral	939	1.592066726415795e-4
+SRR10002774	Human	917187	0.15550829654964043
diff --git a/data/2024-04-12_leung/2/adapters.fasta b/data/2024-04-12_leung/2/adapters.fasta
new file mode 100644
index 0000000..ebb5304
--- /dev/null
+++ b/data/2024-04-12_leung/2/adapters.fasta
@@ -0,0 +1,41 @@
+>0
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
+>1
+AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>2
+CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT
+>3
+GATCGGAAGAGCGGTTCAGCAGGAATGCCGAG
+>4
+GATCGGAAGAGCACACGTCTGAACTCCAGTCAC
+>5
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGATC
+>6
+CTGTCTCTTATACACATCTCCGAGCCCACGAGAC
+>7
+GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG
+>8
+unspecified
+>9
+TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG
+>10
+AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
+>11
+heifigepsna
+>12
+AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
+>13
+GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG
+>14
+TGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
+>15
+ACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>16
+CAAGCAGAAGACGGCATACGAGAT
+>17
+CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT
+>18
+CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATC
+T
+>19
+CTGTCTCTTATACACATCTGACGCTGCCGACGA
diff --git a/data/2024-04-12_leung/2/bracken_counts.tsv b/data/2024-04-12_leung/2/bracken_counts.tsv
new file mode 100644
index 0000000..ab634a2
--- /dev/null
+++ b/data/2024-04-12_leung/2/bracken_counts.tsv
@@ -0,0 +1,401 @@
+name	taxonomy_id	taxonomy_lvl	kraken_assigned_reads	added_reads	new_est_reads	fraction_total_reads	sample
+Eukaryota	2759	D	1107392	1302	1108694	0.51895	SRR10002824
+Bacteria	2	D	1022579	424	1023003	0.47884	SRR10002824
+Archaea	2157	D	3633	1	3634	0.0017	SRR10002824
+Viruses	10239	D	1022	48	1070	5e-4	SRR10002824
+Bacteria	2	D	1029761	189	1029950	0.53563	SRR10002789
+Eukaryota	2759	D	891036	672	891708	0.46374	SRR10002789
+Archaea	2157	D	812	0	812	4.2e-4	SRR10002789
+Viruses	10239	D	373	14	387	2e-4	SRR10002789
+Bacteria	2	D	1187268	451	1187719	0.52052	SRR10002826
+Eukaryota	2759	D	1088894	1492	1090386	0.47786	SRR10002826
+Archaea	2157	D	2949	0	2949	0.00129	SRR10002826
+Viruses	10239	D	712	22	734	3.2e-4	SRR10002826
+Eukaryota	2759	D	569699	799	570498	0.5073	SRR10002857
+Bacteria	2	D	552656	140	552796	0.49156	SRR10002857
+Archaea	2157	D	908	0	908	8.1e-4	SRR10002857
+Viruses	10239	D	368	0	368	3.3e-4	SRR10002857
+Bacteria	2	D	1308914	519	1309433	0.63179	SRR10002783
+Eukaryota	2759	D	759698	1140	760838	0.3671	SRR10002783
+Archaea	2157	D	1763	0	1763	8.5e-4	SRR10002783
+Viruses	10239	D	516	13	529	2.6e-4	SRR10002783
+Eukaryota	2759	D	1685363	816	1686179	0.69973	SRR10002836
+Bacteria	2	D	722099	217	722316	0.29975	SRR10002836
+Archaea	2157	D	565	0	565	2.3e-4	SRR10002836
+Viruses	10239	D	679	25	704	2.9e-4	SRR10002836
+Bacteria	2	D	542655	135	542790	0.62234	SRR10002803
+Eukaryota	2759	D	327837	748	328585	0.37674	SRR10002803
+Archaea	2157	D	630	0	630	7.2e-4	SRR10002803
+Viruses	10239	D	166	6	172	2e-4	SRR10002803
+Eukaryota	2759	D	1642719	909	1643628	0.64572	SRR10002784
+Bacteria	2	D	900069	176	900245	0.35367	SRR10002784
+Archaea	2157	D	1081	0	1081	4.2e-4	SRR10002784
+Viruses	10239	D	446	3	449	1.8e-4	SRR10002784
+Eukaryota	2759	D	860253	728	860981	0.58358	SRR10002830
+Bacteria	2	D	612998	220	613218	0.41564	SRR10002830
+Archaea	2157	D	604	0	604	4.1e-4	SRR10002830
+Viruses	10239	D	538	6	544	3.7e-4	SRR10002830
+Eukaryota	2759	D	321847	420	322267	0.60782	SRR10002845
+Bacteria	2	D	206610	75	206685	0.38983	SRR10002845
+Archaea	2157	D	1107	0	1107	0.00209	SRR10002845
+Viruses	10239	D	137	1	138	2.6e-4	SRR10002845
+Eukaryota	2759	D	782195	977	783172	0.5379	SRR10002853
+Bacteria	2	D	669623	269	669892	0.4601	SRR10002853
+Archaea	2157	D	2381	0	2381	0.00164	SRR10002853
+Viruses	10239	D	512	23	535	3.7e-4	SRR10002853
+Eukaryota	2759	D	281916	459	282375	0.5755	SRR10002850
+Bacteria	2	D	207666	79	207745	0.4234	SRR10002850
+Archaea	2157	D	431	0	431	8.8e-4	SRR10002850
+Viruses	10239	D	109	1	110	2.2e-4	SRR10002850
+Eukaryota	2759	D	1054730	940	1055670	0.7078	SRR10002844
+Bacteria	2	D	434696	151	434847	0.29155	SRR10002844
+Archaea	2157	D	415	0	415	2.8e-4	SRR10002844
+Viruses	10239	D	532	18	550	3.7e-4	SRR10002844
+Eukaryota	2759	D	1410733	1151	1411884	0.63756	SRR10002829
+Bacteria	2	D	800624	325	800949	0.36168	SRR10002829
+Archaea	2157	D	899	0	899	4.1e-4	SRR10002829
+Viruses	10239	D	776	12	788	3.6e-4	SRR10002829
+Bacteria	2	D	103143	40	103183	0.90275	SRR10002817
+Eukaryota	2759	D	11055	17	11072	0.09687	SRR10002817
+Archaea	2157	D	2	0	2	2e-5	SRR10002817
+Viruses	10239	D	15	25	40	3.5e-4	SRR10002817
+Eukaryota	2759	D	1305557	1492	1307049	0.51599	SRR10002825
+Bacteria	2	D	1222579	480	1223059	0.48283	SRR10002825
+Archaea	2157	D	1853	0	1853	7.3e-4	SRR10002825
+Viruses	10239	D	1134	14	1148	4.5e-4	SRR10002825
+Bacteria	2	D	263328	197	263525	0.89614	SRR10002812
+Eukaryota	2759	D	30271	35	30306	0.10306	SRR10002812
+Archaea	2157	D	20	0	20	7e-5	SRR10002812
+Viruses	10239	D	63	150	213	7.2e-4	SRR10002812
+Bacteria	2	D	804873	275	805148	0.56094	SRR10002869
+Eukaryota	2759	D	625378	875	626253	0.43631	SRR10002869
+Archaea	2157	D	3390	1	3391	0.00236	SRR10002869
+Viruses	10239	D	548	9	557	3.9e-4	SRR10002869
+Bacteria	2	D	1096322	470	1096792	0.62977	SRR10002827
+Eukaryota	2759	D	640314	1534	641848	0.36855	SRR10002827
+Archaea	2157	D	2234	1	2235	0.00128	SRR10002827
+Viruses	10239	D	680	12	692	4e-4	SRR10002827
+Eukaryota	2759	D	3205656	2118	3207774	0.79493	SRR10002823
+Bacteria	2	D	825507	317	825824	0.20465	SRR10002823
+Archaea	2157	D	643	0	643	1.6e-4	SRR10002823
+Viruses	10239	D	1049	13	1062	2.6e-4	SRR10002823
+Bacteria	2	D	608912	128	609040	0.51621	SRR10002808
+Eukaryota	2759	D	569102	577	569679	0.48285	SRR10002808
+Archaea	2157	D	690	0	690	5.8e-4	SRR10002808
+Viruses	10239	D	414	4	418	3.5e-4	SRR10002808
+Eukaryota	2759	D	923198	1129	924327	0.50134	SRR10002861
+Bacteria	2	D	917090	393	917483	0.49763	SRR10002861
+Archaea	2157	D	1213	0	1213	6.6e-4	SRR10002861
+Viruses	10239	D	685	5	690	3.7e-4	SRR10002861
+Bacteria	2	D	1057109	381	1057490	0.56613	SRR10002865
+Eukaryota	2759	D	806173	1081	807254	0.43216	SRR10002865
+Archaea	2157	D	2412	0	2412	0.00129	SRR10002865
+Viruses	10239	D	774	11	785	4.2e-4	SRR10002865
+Eukaryota	2759	D	1499683	778	1500461	0.63711	SRR10002847
+Bacteria	2	D	852610	268	852878	0.36214	SRR10002847
+Archaea	2157	D	798	0	798	3.4e-4	SRR10002847
+Viruses	10239	D	953	4	957	4.1e-4	SRR10002847
+Bacteria	2	D	1394599	560	1395159	0.75942	SRR10002828
+Eukaryota	2759	D	435562	1148	436710	0.23771	SRR10002828
+Archaea	2157	D	4702	2	4704	0.00256	SRR10002828
+Viruses	10239	D	532	20	552	3e-4	SRR10002828
+Eukaryota	2759	D	1028832	982	1029814	0.59746	SRR10002866
+Bacteria	2	D	691960	352	692312	0.40165	SRR10002866
+Archaea	2157	D	646	0	646	3.7e-4	SRR10002866
+Viruses	10239	D	861	14	875	5.1e-4	SRR10002866
+Eukaryota	2759	D	1179559	647	1180206	0.62655	SRR10002795
+Bacteria	2	D	702077	178	702255	0.37282	SRR10002795
+Archaea	2157	D	552	0	552	2.9e-4	SRR10002795
+Viruses	10239	D	621	8	629	3.3e-4	SRR10002795
+Bacteria	2	D	942221	389	942610	0.51156	SRR10002860
+Eukaryota	2759	D	896760	1135	897895	0.4873	SRR10002860
+Archaea	2157	D	1372	0	1372	7.4e-4	SRR10002860
+Viruses	10239	D	707	22	729	4e-4	SRR10002860
+Bacteria	2	D	1329143	292	1329435	0.55402	SRR10002841
+Eukaryota	2759	D	1066073	1152	1067225	0.44475	SRR10002841
+Archaea	2157	D	1276	0	1276	5.3e-4	SRR10002841
+Viruses	10239	D	1671	7	1678	7e-4	SRR10002841
+Eukaryota	2759	D	1372193	502	1372695	0.75965	SRR10002859
+Bacteria	2	D	433544	109	433653	0.23999	SRR10002859
+Archaea	2157	D	287	0	287	1.6e-4	SRR10002859
+Viruses	10239	D	345	18	363	2e-4	SRR10002859
+Eukaryota	2759	D	802138	732	802870	0.59809	SRR10002799
+Bacteria	2	D	538298	166	538464	0.40112	SRR10002799
+Archaea	2157	D	655	0	655	4.9e-4	SRR10002799
+Viruses	10239	D	389	12	401	3e-4	SRR10002799
+Bacteria	2	D	43529	32	43561	0.57227	SRR10002815
+Eukaryota	2759	D	32496	12	32508	0.42707	SRR10002815
+Archaea	2157	D	6	0	6	8e-5	SRR10002815
+Viruses	10239	D	27	15	42	5.5e-4	SRR10002815
+Eukaryota	2759	D	554906	599	555505	0.66454	SRR10002843
+Bacteria	2	D	279781	87	279868	0.3348	SRR10002843
+Archaea	2157	D	317	0	317	3.8e-4	SRR10002843
+Viruses	10239	D	232	3	235	2.8e-4	SRR10002843
+Eukaryota	2759	D	1513576	882	1514458	0.68234	SRR10002838
+Bacteria	2	D	703441	216	703657	0.31703	SRR10002838
+Archaea	2157	D	513	0	513	2.3e-4	SRR10002838
+Viruses	10239	D	872	16	888	4e-4	SRR10002838
+Bacteria	2	D	1500019	577	1500596	0.66544	SRR10002780
+Eukaryota	2759	D	747265	1466	748731	0.33203	SRR10002780
+Archaea	2157	D	4702	2	4704	0.00209	SRR10002780
+Viruses	10239	D	976	32	1008	4.5e-4	SRR10002780
+Bacteria	2	D	927058	169	927227	0.70168	SRR10002800
+Eukaryota	2759	D	391974	726	392700	0.29718	SRR10002800
+Archaea	2157	D	1206	0	1206	9.1e-4	SRR10002800
+Viruses	10239	D	298	2	300	2.3e-4	SRR10002800
+Bacteria	2	D	491683	1321	493004	0.72346	SRR10002804
+Eukaryota	2759	D	186633	768	187401	0.275	SRR10002804
+Archaea	2157	D	812	0	812	0.00119	SRR10002804
+Viruses	10239	D	228	5	233	3.4e-4	SRR10002804
+Bacteria	2	D	899956	308	900264	0.57387	SRR10002868
+Eukaryota	2759	D	665392	897	666289	0.42473	SRR10002868
+Archaea	2157	D	1570	0	1570	0.001	SRR10002868
+Viruses	10239	D	612	12	624	4e-4	SRR10002868
+Bacteria	2	D	1172617	322	1172939	0.50964	SRR10002781
+Eukaryota	2759	D	1125450	1496	1126946	0.48966	SRR10002781
+Archaea	2157	D	1106	0	1106	4.8e-4	SRR10002781
+Viruses	10239	D	500	13	513	2.2e-4	SRR10002781
+Bacteria	2	D	1869057	410	1869467	0.54041	SRR10002818
+Eukaryota	2759	D	1585255	1344	1586599	0.45864	SRR10002818
+Archaea	2157	D	1981	0	1981	5.7e-4	SRR10002818
+Viruses	10239	D	1274	16	1290	3.7e-4	SRR10002818
+Bacteria	2	D	743861	323	744184	0.52455	SRR10002867
+Eukaryota	2759	D	672066	842	672908	0.47431	SRR10002867
+Archaea	2157	D	969	0	969	6.8e-4	SRR10002867
+Viruses	10239	D	615	21	636	4.5e-4	SRR10002867
+Eukaryota	2759	D	1280920	1300	1282220	0.58161	SRR10002833
+Bacteria	2	D	919269	457	919726	0.41718	SRR10002833
+Archaea	2157	D	1655	0	1655	7.5e-4	SRR10002833
+Viruses	10239	D	973	29	1002	4.5e-4	SRR10002833
+Bacteria	2	D	637233	143	637376	0.51723	SRR10002805
+Eukaryota	2759	D	592745	775	593520	0.48164	SRR10002805
+Archaea	2157	D	907	0	907	7.4e-4	SRR10002805
+Viruses	10239	D	478	1	479	3.9e-4	SRR10002805
+Eukaryota	2759	D	886584	962	887546	0.53659	SRR10002786
+Bacteria	2	D	764141	280	764421	0.46215	SRR10002786
+Archaea	2157	D	1426	0	1426	8.6e-4	SRR10002786
+Viruses	10239	D	645	19	664	4e-4	SRR10002786
+Eukaryota	2759	D	1790474	978	1791452	0.61297	SRR10002791
+Bacteria	2	D	1128757	272	1129029	0.38631	SRR10002791
+Archaea	2157	D	900	0	900	3.1e-4	SRR10002791
+Viruses	10239	D	1186	14	1200	4.1e-4	SRR10002791
+Bacteria	2	D	581929	261	582190	0.52145	SRR10002855
+Eukaryota	2759	D	531845	728	532573	0.47701	SRR10002855
+Archaea	2157	D	1454	0	1454	0.0013	SRR10002855
+Viruses	10239	D	245	24	269	2.4e-4	SRR10002855
+Eukaryota	2759	D	1317723	918	1318641	0.51452	SRR10002797
+Bacteria	2	D	1238637	279	1238916	0.48341	SRR10002797
+Archaea	2157	D	4462	1	4463	0.00174	SRR10002797
+Viruses	10239	D	823	3	826	3.2e-4	SRR10002797
+Bacteria	2	D	781718	265	781983	0.53047	SRR10002778
+Eukaryota	2759	D	689969	996	690965	0.46872	SRR10002778
+Archaea	2157	D	799	0	799	5.4e-4	SRR10002778
+Viruses	10239	D	382	7	389	2.6e-4	SRR10002778
+Eukaryota	2759	D	1728921	1091	1730012	0.57707	SRR10002870
+Bacteria	2	D	1256778	391	1257169	0.41935	SRR10002870
+Archaea	2157	D	9398	2	9400	0.00314	SRR10002870
+Viruses	10239	D	1315	11	1326	4.4e-4	SRR10002870
+Eukaryota	2759	D	661193	668	661861	0.55717	SRR10002802
+Bacteria	2	D	524891	129	525020	0.44198	SRR10002802
+Archaea	2157	D	737	0	737	6.2e-4	SRR10002802
+Viruses	10239	D	263	6	269	2.3e-4	SRR10002802
+Eukaryota	2759	D	1309775	1398	1311173	0.51975	SRR10002776
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+Archaea	2157	D	1609	0	1609	6.4e-4	SRR10002776
+Viruses	10239	D	949	31	980	3.9e-4	SRR10002776
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+Eukaryota	2759	D	580118	1111	581229	0.30712	SRR10002779
+Archaea	2157	D	2976	1	2977	0.00157	SRR10002779
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+Eukaryota	2759	D	274744	833	275577	0.33958	SRR10002872
+Archaea	2157	D	1097	0	1097	0.00135	SRR10002872
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+Eukaryota	2759	D	348552	923	349475	0.35586	SRR10002801
+Archaea	2157	D	1031	0	1031	0.00105	SRR10002801
+Viruses	10239	D	507	1	508	5.2e-4	SRR10002801
+Eukaryota	2759	D	2279389	772	2280161	0.77699	SRR10002794
+Bacteria	2	D	653129	129	653258	0.22261	SRR10002794
+Archaea	2157	D	498	0	498	1.7e-4	SRR10002794
+Viruses	10239	D	682	6	688	2.3e-4	SRR10002794
+Eukaryota	2759	D	2090547	1019	2091566	0.6574	SRR10002821
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+Archaea	2157	D	1447	0	1447	4.5e-4	SRR10002821
+Viruses	10239	D	763	2	765	2.4e-4	SRR10002821
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+Bacteria	2	D	420541	100	420641	0.12627	SRR10002792
+Archaea	2157	D	380	0	380	1.1e-4	SRR10002792
+Viruses	10239	D	378	1	379	1.1e-4	SRR10002792
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+Eukaryota	2759	D	1230588	1715	1232303	0.43382	SRR10002862
+Archaea	2157	D	1520	0	1520	5.4e-4	SRR10002862
+Viruses	10239	D	1302	37	1339	4.7e-4	SRR10002862
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+Eukaryota	2759	D	593390	1314	594704	0.28447	SRR10002864
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+Eukaryota	2759	D	30363	25	30388	0.08668	SRR10002813
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+Viruses	10239	D	77	237	314	9e-4	SRR10002813
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+Eukaryota	2759	D	924735	1185	925920	0.46501	SRR10002822
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+Viruses	10239	D	888	18	906	4.6e-4	SRR10002822
+Eukaryota	2759	D	1045798	807	1046605	0.71666	SRR10002849
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+Archaea	2157	D	617	0	617	4.2e-4	SRR10002849
+Viruses	10239	D	366	35	401	2.7e-4	SRR10002849
+Bacteria	2	D	39019	25	39044	0.71601	SRR10002814
+Eukaryota	2759	D	15440	8	15448	0.28329	SRR10002814
+Archaea	2157	D	10	0	10	1.8e-4	SRR10002814
+Viruses	10239	D	20	6	26	4.8e-4	SRR10002814
+Eukaryota	2759	D	971514	680	972194	0.53659	SRR10002798
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+Archaea	2157	D	1233	0	1233	6.8e-4	SRR10002798
+Viruses	10239	D	797	6	803	4.4e-4	SRR10002798
+Eukaryota	2759	D	1493477	613	1494090	0.67283	SRR10002858
+Bacteria	2	D	724720	168	724888	0.32644	SRR10002858
+Archaea	2157	D	1106	0	1106	5e-4	SRR10002858
+Viruses	10239	D	530	3	533	2.4e-4	SRR10002858
+Eukaryota	2759	D	1503359	1040	1504399	0.55114	SRR10002810
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+Viruses	10239	D	1213	6	1219	4.5e-4	SRR10002810
+Bacteria	2	D	878429	192	878621	0.63358	SRR10002806
+Eukaryota	2759	D	505211	856	506067	0.36493	SRR10002806
+Archaea	2157	D	1735	0	1735	0.00125	SRR10002806
+Viruses	10239	D	335	6	341	2.5e-4	SRR10002806
+Bacteria	2	D	126632	83	126715	0.82559	SRR10002816
+Eukaryota	2759	D	26619	35	26654	0.17366	SRR10002816
+Archaea	2157	D	12	0	12	8e-5	SRR10002816
+Viruses	10239	D	35	68	103	6.7e-4	SRR10002816
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+Eukaryota	2759	D	99618	631	100249	0.16331	SRR10002831
+Archaea	2157	D	1245	1	1246	0.00203	SRR10002831
+Viruses	10239	D	578	3	581	9.5e-4	SRR10002831
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+Eukaryota	2759	D	1274622	916	1275538	0.42517	SRR10002788
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+Viruses	10239	D	1108	12	1120	3.7e-4	SRR10002788
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+Eukaryota	2759	D	148322	565	148887	0.28299	SRR10002871
+Archaea	2157	D	832	0	832	0.00158	SRR10002871
+Viruses	10239	D	144	12	156	3e-4	SRR10002871
+Bacteria	2	D	1171812	269	1172081	0.68083	SRR10002807
+Eukaryota	2759	D	546962	864	547826	0.31822	SRR10002807
+Archaea	2157	D	1086	0	1086	6.3e-4	SRR10002807
+Viruses	10239	D	552	4	556	3.2e-4	SRR10002807
+Bacteria	2	D	425749	174	425923	0.59488	SRR10002840
+Eukaryota	2759	D	288328	536	288864	0.40345	SRR10002840
+Archaea	2157	D	460	0	460	6.4e-4	SRR10002840
+Viruses	10239	D	717	12	729	0.00102	SRR10002840
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+Eukaryota	2759	D	412072	583	412655	0.3399	SRR10002854
+Archaea	2157	D	1221	0	1221	0.00101	SRR10002854
+Viruses	10239	D	283	15	298	2.5e-4	SRR10002854
+Eukaryota	2759	D	3187943	666	3188609	0.97483	SRR10002839
+Bacteria	2	D	82187	21	82208	0.02513	SRR10002839
+Archaea	2157	D	66	0	66	2e-5	SRR10002839
+Viruses	10239	D	57	2	59	2e-5	SRR10002839
+Eukaryota	2759	D	731744	794	732538	0.53145	SRR10002809
+Bacteria	2	D	643705	139	643844	0.4671	SRR10002809
+Archaea	2157	D	1580	0	1580	0.00115	SRR10002809
+Viruses	10239	D	415	2	417	3e-4	SRR10002809
+Eukaryota	2759	D	1359821	664	1360485	0.6305	SRR10002793
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+Archaea	2157	D	671	0	671	3.1e-4	SRR10002793
+Viruses	10239	D	734	4	738	3.4e-4	SRR10002793
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+Bacteria	2	D	785283	233	785516	0.37651	SRR10002848
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+Viruses	10239	D	687	40	727	3.5e-4	SRR10002848
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+Eukaryota	2759	D	2780491	771	2781262	0.9145	SRR10002790
+Bacteria	2	D	259346	47	259393	0.08529	SRR10002790
+Archaea	2157	D	454	0	454	1.5e-4	SRR10002790
+Viruses	10239	D	178	0	178	6e-5	SRR10002790
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+Eukaryota	2759	D	86063	519	86582	0.14008	SRR10002837
+Archaea	2157	D	1540	1	1541	0.00249	SRR10002837
+Viruses	10239	D	381	2	383	6.2e-4	SRR10002837
+Bacteria	2	D	989530	301	989831	0.5572	SRR10002782
+Eukaryota	2759	D	783907	1011	784918	0.44185	SRR10002782
+Archaea	2157	D	1101	0	1101	6.2e-4	SRR10002782
+Viruses	10239	D	575	9	584	3.3e-4	SRR10002782
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+Eukaryota	2759	D	794202	1083	795285	0.41729	SRR10002832
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+Eukaryota	2759	D	435103	875	435978	0.31146	SRR10002796
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+Eukaryota	2759	D	34279	388	34667	0.0611	SRR10002834
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+Viruses	10239	D	199	0	199	3.5e-4	SRR10002834
+Eukaryota	2759	D	2127377	686	2128063	0.89153	SRR10002873
+Bacteria	2	D	258309	108	258417	0.10826	SRR10002873
+Archaea	2157	D	197	0	197	8e-5	SRR10002873
+Viruses	10239	D	255	37	292	1.2e-4	SRR10002873
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+Eukaryota	2759	D	922188	1334	923522	0.44585	SRR10002775
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+Viruses	10239	D	650	14	664	3.2e-4	SRR10002775
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+Eukaryota	2759	D	153092	491	153583	0.286	SRR10002856
+Archaea	2157	D	635	0	635	0.00118	SRR10002856
+Viruses	10239	D	267	1	268	5e-4	SRR10002856
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+Eukaryota	2759	D	903496	1035	904531	0.4778	SRR10002785
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+Eukaryota	2759	D	1127566	1455	1129021	0.40423	SRR10002819
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+Viruses	10239	D	1021	19	1040	3.7e-4	SRR10002819
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+Eukaryota	2759	D	435241	965	436206	0.4372	SRR10002851
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@@ -0,0 +1,101 @@
+sample,library,country,region,city,location,instrument,date
+SRR10002775,SRR10002775,Norway,Europe,Oslo,"Oslo, Jernbanetorget",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002776,SRR10002776,United Kingdom,Europe,London,"London, Kings Cross St Pancras",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002777,SRR10002777,United Kingdom,Europe,London,"London, Vauxhall",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002778,SRR10002778,United Kingdom,Europe,London,"London, Green Park",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002779,SRR10002779,United Kingdom,Europe,London,"London, Euston",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002780,SRR10002780,USA,North America,New York City,"New York City, 28th St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002781,SRR10002781,United Kingdom,Europe,London,"London, Green Park",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002782,SRR10002782,United Kingdom,Europe,London,"London, Oxford Circus",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002783,SRR10002783,United Kingdom,Europe,London,"London, Kings Cross St Pancras",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002784,SRR10002784,Hong Kong,Asia,Hong Kong,"Hong Kong, East Tsim Sha Tsui",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002785,SRR10002785,United Kingdom,Europe,London,"London, Euston",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002786,SRR10002786,United Kingdom,Europe,London,"London, Brixton",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002787,SRR10002787,Hong Kong,Asia,Hong Kong,"Hong Kong, Hung Hom",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002788,SRR10002788,Hong Kong,Asia,Hong Kong,"Hong Kong, Admiralty",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002789,SRR10002789,Hong Kong,Asia,Hong Kong,"Hong Kong, Ocean Park",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002790,SRR10002790,Hong Kong,Asia,Hong Kong,"Hong Kong, Mong Kok East",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002791,SRR10002791,Hong Kong,Asia,Hong Kong,"Hong Kong, Tsing Yi",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002792,SRR10002792,Hong Kong,Asia,Hong Kong,"Hong Kong, Lai King",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002793,SRR10002793,Hong Kong,Asia,Hong Kong,"Hong Kong, Cheung Sha Wan",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002794,SRR10002794,Hong Kong,Asia,Hong Kong,"Hong Kong, Sham Shui Po",SASS 3100 electret filter air sampler,2017-07-06
+SRR10002795,SRR10002795,Hong Kong,Asia,Hong Kong,"Hong Kong, Fortress Hill",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002796,SRR10002796,Hong Kong,Asia,Hong Kong,"Hong Kong, North Point",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002797,SRR10002797,Hong Kong,Asia,Hong Kong,"Hong Kong, Quarry Bay",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002798,SRR10002798,Hong Kong,Asia,Hong Kong,"Hong Kong, Yau Tong",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002799,SRR10002799,Norway,Europe,Oslo,"Oslo, Vestli",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002800,SRR10002800,Norway,Europe,Oslo,"Oslo, Romsaas",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002801,SRR10002801,Norway,Europe,Oslo,"Oslo, Nydalen",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002802,SRR10002802,Norway,Europe,Oslo,"Oslo, Loeren",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002803,SRR10002803,Norway,Europe,Oslo,"Oslo, Majorstua",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002804,SRR10002804,Norway,Europe,Oslo,"Oslo, Forskningsparken",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002805,SRR10002805,Norway,Europe,Oslo,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002806,SRR10002806,Norway,Europe,Oslo,"Oslo, Toeyen",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002807,SRR10002807,Hong Kong,Asia,Hong Kong,"Hong Kong, Che Kung Temple",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002808,SRR10002808,Norway,Europe,Oslo,"Oslo, Carl Berners plass",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002809,SRR10002809,Norway,Europe,Oslo,"Oslo, Groenland",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002810,SRR10002810,Hong Kong,Asia,Hong Kong,"Hong Kong, Fortress Hill",SASS 3100 electret filter air sampler,2017-07-05
+SRR10002811,SRR10002811,Norway,Europe,Oslo,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002812,SRR10002812,uncalculated,uncalculated,,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002813,SRR10002813,uncalculated,uncalculated,,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002814,SRR10002814,uncalculated,uncalculated,,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002815,SRR10002815,uncalculated,uncalculated,,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002816,SRR10002816,uncalculated,uncalculated,,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002817,SRR10002817,uncalculated,uncalculated,,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002818,SRR10002818,Hong Kong,Asia,Hong Kong,"Hong Kong, Hung Hom",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002819,SRR10002819,Hong Kong,Asia,Hong Kong,"Hong Kong, Mong Kok East",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002820,SRR10002820,Hong Kong,Asia,Hong Kong,"Hong Kong, Tsim Sha Tsui",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002821,SRR10002821,Hong Kong,Asia,Hong Kong,"Hong Kong, East Tsim Sha Tsui",SASS 3100 electret filter air sampler,2017-07-14
+SRR10002822,SRR10002822,USA,North America,New York City,"New York City, Time Square 42nd St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002823,SRR10002823,USA,North America,New York City,"New York City, Grand Central 42nd St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002824,SRR10002824,USA,North America,New York City,"New York City, 59th St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002825,SRR10002825,USA,North America,New York City,"New York City, 77th St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002826,SRR10002826,USA,North America,New York City,"New York City, 39th St. Herald Sq.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002827,SRR10002827,USA,North America,New York City,"New York City, Union Square 14th St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002828,SRR10002828,USA,North America,New York City,"New York City, 33rd St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002829,SRR10002829,USA,North America,New York City,"New York City, 51st St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002830,SRR10002830,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002831,SRR10002831,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002832,SRR10002832,USA,North America,New York City,"New York City, Canal St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002833,SRR10002833,USA,North America,New York City,"New York City, 86th St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002834,SRR10002834,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002835,SRR10002835,USA,North America,Denver,"Denver, Train Cabin - front wagon",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002836,SRR10002836,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002837,SRR10002837,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002838,SRR10002838,Hong Kong,Asia,Hong Kong,"Hong Kong, Che Kung Temple",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002839,SRR10002839,Hong Kong,Asia,Hong Kong,"Hong Kong, Sha Tin Wai",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002840,SRR10002840,Hong Kong,Asia,Hong Kong,"Hong Kong, Kowloon Tong",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002841,SRR10002841,Hong Kong,Asia,Hong Kong,"Hong Kong, Tai Wai",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002842,SRR10002842,Sweden,Europe,Stockholm,"Stockholm, Universitetet",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002843,SRR10002843,Sweden,Europe,Stockholm,"Stockholm, Danderyd",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002844,SRR10002844,Sweden,Europe,Stockholm,"Stockholm, T-centralen",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002845,SRR10002845,Sweden,Europe,Stockholm,"Stockholm, Tekniska hoegskolan",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002846,SRR10002846,Hong Kong,Asia,Hong Kong,"Hong Kong, Shek Kip Mei",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002847,SRR10002847,Hong Kong,Asia,Hong Kong,"Hong Kong, Prince Edward",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002848,SRR10002848,Norway,Europe,Oslo,"Oslo, Loeren",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002849,SRR10002849,Norway,Europe,Oslo,"Oslo, Nydalen",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002850,SRR10002850,Norway,Europe,Oslo,"Oslo, Forskningsparken",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002851,SRR10002851,Norway,Europe,Oslo,"Oslo, Majorstua",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002852,SRR10002852,Norway,Europe,Oslo,"Oslo, Toeyen",Bobcat ACD-200 electret filter air sampler,2017-06-21
+SRR10002853,SRR10002853,Norway,Europe,Oslo,"Oslo, Helsfyr",Bobcat ACD-200 electret filter air sampler,2017-06-21
+SRR10002854,SRR10002854,Norway,Europe,Oslo,"Oslo, Lindeberg",Bobcat ACD-200 electret filter air sampler,2017-06-21
+SRR10002855,SRR10002855,Norway,Europe,Oslo,"Oslo, Ellingsrudaasen",Bobcat ACD-200 electret filter air sampler,2017-06-21
+SRR10002856,SRR10002856,Norway,Europe,Oslo,"Oslo, Forskningsparken",Bobcat ACD-200 electret filter air sampler,2017-06-21
+SRR10002857,SRR10002857,Norway,Europe,Oslo,"Oslo, Majorstua",Bobcat ACD-200 electret filter air sampler,2017-06-21
+SRR10002858,SRR10002858,Hong Kong,Asia,Hong Kong,"Hong Kong, Quarry Bay",SASS 3100 electret filter air sampler,2017-07-12
+SRR10002859,SRR10002859,USA,North America,Denver,"Denver, Denver Airport",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002860,SRR10002860,USA,North America,New York City,"New York City, 68th St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002861,SRR10002861,USA,North America,New York City,"New York City, 57th St.",SASS 3100 electret filter air sampler,2017-06-23
+SRR10002862,SRR10002862,USA,North America,New York City,"New York City, 96th St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002863,SRR10002863,USA,North America,New York City,"New York City, 28th St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002864,SRR10002864,USA,North America,New York City,"New York City, 33rd St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002865,SRR10002865,USA,North America,New York City,"New York City, 86th St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002866,SRR10002866,USA,North America,New York City,"New York City, Grand Central 42nd St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002867,SRR10002867,USA,North America,New York City,"New York City, 51st St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002868,SRR10002868,USA,North America,New York City,"New York City, 77th St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002869,SRR10002869,USA,North America,New York City,"New York City, 59th St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002870,SRR10002870,Hong Kong,Asia,Hong Kong,"Hong Kong, Quarry Bay",SASS 3100 electret filter air sampler,2017-07-05
+SRR10002871,SRR10002871,USA,North America,Denver,"Denver, Denver Airport",SASS 3100 electret filter air sampler,2017-06-29
+SRR10002872,SRR10002872,USA,North America,Denver,"Denver, Denver Airport",SASS 3100 electret filter air sampler,2017-06-29
+SRR10002873,SRR10002873,USA,North America,Denver,"Denver, Boulder Central",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002874,SRR10002874,USA,North America,Denver,"Denver, Boulder Central",SASS 3100 electret filter air sampler,2017-06-27
diff --git a/data/2024-04-12_leung/2/taxonomic_composition.tsv b/data/2024-04-12_leung/2/taxonomic_composition.tsv
new file mode 100644
index 0000000..4601d83
--- /dev/null
+++ b/data/2024-04-12_leung/2/taxonomic_composition.tsv
@@ -0,0 +1,801 @@
+sample	classification	n_reads	p_reads
+SRR10002775	Filtered	423580	0.07354294409291712
+SRR10002775	Duplicate	500944	0.0869750615838384
+SRR10002775	Ribosomal	17017	0.002954531091244087
+SRR10002775	Unassigned	2746702	0.4768887851784872
+SRR10002775	Bacterial	1141644	0.19821488471130427
+SRR10002775	Archaeal	5555	9.644720110396018e-4
+SRR10002775	Viral	664	1.1528522328178139e-4
+SRR10002775	Human	923522	0.16034403610788753
+SRR10002776	Filtered	367603	0.06076302596738626
+SRR10002776	Duplicate	384727	0.06359354165051595
+SRR10002776	Ribosomal	15038	0.002485709813297374
+SRR10002776	Unassigned	2759692	0.4561639503975433
+SRR10002776	Bacterial	1208959	0.1998351675870581
+SRR10002776	Archaeal	1609	2.6596004053700456e-4
+SRR10002776	Viral	980	1.619893348205497e-4
+SRR10002776	Human	1311173	0.21673065520884144
+SRR10002777	Filtered	292208	0.08578711930806157
+SRR10002777	Duplicate	233894	0.0686671565577936
+SRR10002777	Ribosomal	10088	0.0029616590222708654
+SRR10002777	Unassigned	1682088	0.49383139387921843
+SRR10002777	Bacterial	694162	0.20379373019603375
+SRR10002777	Archaeal	1074	3.1530747322748906e-4
+SRR10002777	Viral	414	1.2154310420500975e-4
+SRR10002777	Human	492271	0.14452209045918926
+SRR10002778	Filtered	295701	0.07669338260505558
+SRR10002778	Duplicate	188047	0.04877210600820723
+SRR10002778	Ribosomal	10243	0.0026566373398249727
+SRR10002778	Unassigned	1887499	0.4895441103468023
+SRR10002778	Bacterial	781983	0.20281609263969066
+SRR10002778	Archaeal	799	2.072296431240997e-4
+SRR10002778	Viral	389	1.0089152837956793e-4
+SRR10002778	Human	690965	0.17920954988891558
+SRR10002779	Filtered	520309	0.08736655757369988
+SRR10002779	Duplicate	293549	0.04929064384664118
+SRR10002779	Ribosomal	20642	0.0034660566729314943
+SRR10002779	Unassigned	3228456	0.5420991891321442
+SRR10002779	Bacterial	1307695	0.21957877051202163
+SRR10002779	Archaeal	2977	4.998765001122497e-4
+SRR10002779	Viral	614	1.0309847869295308e-4
+SRR10002779	Human	581229	0.0975958072837564
+SRR10002780	Filtered	635366	0.08926973477433317
+SRR10002780	Duplicate	409347	0.057513776501526614
+SRR10002780	Ribosomal	16185	0.002274013178738841
+SRR10002780	Unassigned	3801436	0.5341066148985025
+SRR10002780	Bacterial	1500596	0.21083565523403086
+SRR10002780	Archaeal	4704	6.609180100579245e-4
+SRR10002780	Viral	1008	1.4162528786955525e-4
+SRR10002780	Human	748731	0.10519766211494044
+SRR10002781	Filtered	456341	0.07470456858509032
+SRR10002781	Duplicate	365387	0.0598150904731339
+SRR10002781	Ribosomal	17766	0.002908354422422519
+SRR10002781	Unassigned	2967611	0.4858079801801032
+SRR10002781	Bacterial	1172939	0.19201409027816316
+SRR10002781	Archaeal	1106	1.8105594907122063e-4
+SRR10002781	Viral	513	8.397983894533108e-5
+SRR10002781	Human	1126946	0.18448488027307036
+SRR10002782	Filtered	385318	0.0786244526336839
+SRR10002782	Duplicate	270066	0.05510718789407314
+SRR10002782	Ribosomal	14777	0.003015258920081457
+SRR10002782	Unassigned	2454145	0.5007702918334782
+SRR10002782	Bacterial	989831	0.2019758240592237
+SRR10002782	Archaeal	1101	2.246599493137771e-4
+SRR10002782	Viral	584	1.1916567702020512e-4
+SRR10002782	Human	784918	0.1601631590331256
+SRR10002783	Filtered	527781	0.079639764216768
+SRR10002783	Duplicate	381806	0.05761279738480036
+SRR10002783	Ribosomal	26267	0.00396357141822431
+SRR10002783	Unassigned	3618687	0.5460434904899636
+SRR10002783	Bacterial	1309433	0.19758751333915991
+SRR10002783	Archaeal	1763	2.6602872084095856e-4
+SRR10002783	Viral	529	7.982370579969772e-5
+SRR10002783	Human	760838	0.11480701072444313
+SRR10002784	Filtered	302051	0.06609490369607254
+SRR10002784	Duplicate	390109	0.08536378553943264
+SRR10002784	Ribosomal	8179	0.0017897315927779657
+SRR10002784	Unassigned	1324217	0.2897656193414427
+SRR10002784	Bacterial	900245	0.1969919204964421
+SRR10002784	Archaeal	1081	2.3654479175852562e-4
+SRR10002784	Viral	449	9.825033441219057e-5
+SRR10002784	Human	1643628	0.3596592442076614
+SRR10002785	Filtered	355080	0.07550851501621685
+SRR10002785	Duplicate	292494	0.06219946939042844
+SRR10002785	Ribosomal	12173	0.0025886142652146215
+SRR10002785	Unassigned	2149674	0.4571327349019121
+SRR10002785	Bacterial	986405	0.20976111511369658
+SRR10002785	Archaeal	1552	3.300360913179243e-4
+SRR10002785	Viral	607	1.2907983726158508e-4
+SRR10002785	Human	904531	0.1923504353839519
+SRR10002786	Filtered	312797	0.07664827221179434
+SRR10002786	Duplicate	203448	0.04985322009144952
+SRR10002786	Ribosomal	11467	0.002809891838645018
+SRR10002786	Unassigned	1899171	0.4653758692850177
+SRR10002786	Bacterial	764421	0.18731493234401878
+SRR10002786	Archaeal	1426	3.4942929815189635e-4
+SRR10002786	Viral	664	1.6270761148166844e-4
+SRR10002786	Human	887546	0.2174856773194411
+SRR10002787	Filtered	299470	0.06212799857267629
+SRR10002787	Duplicate	472060	0.09793349252418464
+SRR10002787	Ribosomal	12343	0.0025606768169851523
+SRR10002787	Unassigned	1575070	0.3267637717028926
+SRR10002787	Bacterial	1134569	0.23537750429960522
+SRR10002787	Archaeal	1737	3.6035774374975363e-4
+SRR10002787	Viral	962	1.9957636700475704e-4
+SRR10002787	Human	1323999	0.2746766219729016
+SRR10002788	Filtered	295123	0.05382781512033345
+SRR10002788	Duplicate	568155	0.10362642796289362
+SRR10002788	Ribosomal	13451	0.0024533429830396318
+SRR10002788	Unassigned	1605961	0.29291302879974057
+SRR10002788	Bacterial	1722200	0.3141139904386926
+SRR10002788	Archaeal	1175	2.1430956843889433e-4
+SRR10002788	Viral	1120	2.0427805672473332e-4
+SRR10002788	Human	1275538	0.2326468070701365
+SRR10002789	Filtered	220996	0.05354542551454208
+SRR10002789	Duplicate	404084	0.09790606944749328
+SRR10002789	Ribosomal	9725	0.0023562836573011356
+SRR10002789	Unassigned	1569600	0.38030054791772366
+SRR10002789	Bacterial	1029950	0.24954800543314187
+SRR10002789	Archaeal	812	1.9674059945794573e-4
+SRR10002789	Viral	387	9.376676353475984e-5
+SRR10002789	Human	891708	0.21605316066680524
+SRR10002790	Filtered	215563	0.0523442775771901
+SRR10002790	Duplicate	332486	0.08073620924986954
+SRR10002790	Ribosomal	4942	0.0012000455541371826
+SRR10002790	Unassigned	523899	0.1272162415554261
+SRR10002790	Bacterial	259393	0.06298733638694985
+SRR10002790	Archaeal	454	1.1024295458888727e-4
+SRR10002790	Viral	178	4.322300862736109e-5
+SRR10002790	Human	2781262	0.675362423713211
+SRR10002791	Filtered	381295	0.07059106023682843
+SRR10002791	Duplicate	475416	0.08801615414194265
+SRR10002791	Ribosomal	13671	0.0025309809583070365
+SRR10002791	Unassigned	1608500	0.2977896914224905
+SRR10002791	Bacterial	1129029	0.20902281474482007
+SRR10002791	Archaeal	900	1.6662152457584177e-4
+SRR10002791	Viral	1200	2.2216203276778903e-4
+SRR10002791	Human	1791452	0.33166051493826765
+SRR10002792	Filtered	307801	0.06569935109828338
+SRR10002792	Duplicate	444222	0.0948180712329773
+SRR10002792	Ribosomal	7725	0.0016488818659921157
+SRR10002792	Unassigned	593884	0.12676304959260346
+SRR10002792	Bacterial	420641	0.08978476595375916
+SRR10002792	Archaeal	380	8.111004648246005e-5
+SRR10002792	Viral	379	8.089659899171675e-5
+SRR10002792	Human	2909961	0.6211238736109104
+SRR10002793	Filtered	242074	0.06526476588569702
+SRR10002793	Duplicate	331856	0.08947059224767578
+SRR10002793	Ribosomal	8866	0.0023903327674289257
+SRR10002793	Unassigned	968536	0.26112376914443286
+SRR10002793	Bacterial	795881	0.21457482892782548
+SRR10002793	Archaeal	671	1.8090607793196583e-4
+SRR10002793	Viral	738	1.989697250578104e-4
+SRR10002793	Human	1360485	0.36679583522395015
+SRR10002794	Filtered	296345	0.0655764730614119
+SRR10002794	Duplicate	405507	0.08973230141124013
+SRR10002794	Ribosomal	8389	0.0018563533466472674
+SRR10002794	Unassigned	874229	0.19345308497867372
+SRR10002794	Bacterial	653258	0.14455568894076776
+SRR10002794	Archaeal	498	1.1019954304807953e-4
+SRR10002794	Viral	688	1.5224354541582073e-4
+SRR10002794	Human	2280161	0.5045636551727953
+SRR10002795	Filtered	195589	0.05968991845886367
+SRR10002795	Duplicate	298949	0.09123335889727355
+SRR10002795	Ribosomal	7110	0.0021698322515198744
+SRR10002795	Unassigned	891461	0.272056375354734
+SRR10002795	Bacterial	702255	0.21431442303672144
+SRR10002795	Archaeal	552	1.6845955032896914e-4
+SRR10002795	Viral	629	1.9195843687848117e-4
+SRR10002795	Human	1180206	0.36017567401368
+SRR10002796	Filtered	313492	0.08013906463693649
+SRR10002796	Duplicate	404688	0.1034518194716055
+SRR10002796	Ribosomal	8365	0.002138374426422281
+SRR10002796	Unassigned	1785518	0.45643825811316896
+SRR10002796	Bacterial	958455	0.24501322903485562
+SRR10002796	Archaeal	4934	0.0012612958063320423
+SRR10002796	Viral	420	1.0736607998772959e-4
+SRR10002796	Human	435978	0.11145059243069136
+SRR10002797	Filtered	253307	0.05282730910796473
+SRR10002797	Duplicate	459415	0.09581124174948034
+SRR10002797	Ribosomal	11898	0.0024813342061868185
+SRR10002797	Unassigned	1507535	0.3143972232748231
+SRR10002797	Bacterial	1238916	0.2583765884511807
+SRR10002797	Archaeal	4463	9.307610154825828e-4
+SRR10002797	Viral	826	1.7226273779713498e-4
+SRR10002797	Human	1318641	0.2750032794570846
+SRR10002798	Filtered	192327	0.056400846685720415
+SRR10002798	Duplicate	316576	0.0928374822067553
+SRR10002798	Ribosomal	8096	0.0023741921558990287
+SRR10002798	Unassigned	1081212	0.3170707817766676
+SRR10002798	Bacterial	837561	0.24561891752556156
+SRR10002798	Archaeal	1233	3.6158336564025474e-4
+SRR10002798	Viral	803	2.3548373285411562e-4
+SRR10002798	Human	972194	0.28510071255090175
+SRR10002799	Filtered	562188	0.14210870359819233
+SRR10002799	Duplicate	209718	0.05301207621152657
+SRR10002799	Ribosomal	11102	0.0028063402764682477
+SRR10002799	Unassigned	1830644	0.46274635102458467
+SRR10002799	Bacterial	538464	0.13611180063305697
+SRR10002799	Archaeal	655	1.655695263093769e-4
+SRR10002799	Viral	401	1.013639390077254e-4
+SRR10002799	Human	802870	0.2029477947908541
+SRR10002800	Filtered	880308	0.17700293701965794
+SRR10002800	Duplicate	337323	0.06782530855596232
+SRR10002800	Ribosomal	17226	0.003463620225081026
+SRR10002800	Unassigned	2417119	0.48600849035339744
+SRR10002800	Bacterial	927227	0.1864369087682111
+SRR10002800	Archaeal	1206	2.4248960823451278e-4
+SRR10002800	Viral	300	6.032079806828676e-5
+SRR10002800	Human	392700	0.07895992467138738
+SRR10002801	Filtered	295494	0.0764175352710938
+SRR10002801	Duplicate	333045	0.08612857802311193
+SRR10002801	Ribosomal	13154	0.003401748458364528
+SRR10002801	Unassigned	2243100	0.580086815186063
+SRR10002801	Bacterial	631028	0.1631897921685306
+SRR10002801	Archaeal	1031	2.666263235953952e-4
+SRR10002801	Viral	508	1.313735910634925e-4
+SRR10002801	Human	349475	0.09037753097817725
+SRR10002802	Filtered	311960	0.08498741779750296
+SRR10002802	Duplicate	350498	0.09548634428513011
+SRR10002802	Ribosomal	11971	0.0032612654777981406
+SRR10002802	Unassigned	1808345	0.492648326827239
+SRR10002802	Bacterial	525020	0.14303145945648482
+SRR10002802	Archaeal	737	2.0078127617886805e-4
+SRR10002802	Viral	269	7.328380365280259e-5
+SRR10002802	Human	661861	0.1803111210760133
+SRR10002803	Filtered	191576	0.055890734512592743
+SRR10002803	Duplicate	305104	0.08901160198944595
+SRR10002803	Ribosomal	9274	0.0027056138131591905
+SRR10002803	Unassigned	2049557	0.5979415279336976
+SRR10002803	Bacterial	542790	0.15835455268974305
+SRR10002803	Archaeal	630	1.8379735845269464e-4
+SRR10002803	Viral	172	5.0179596275973775e-5
+SRR10002803	Human	328585	0.0958619921066328
+SRR10002804	Filtered	224687	0.06598859244679123
+SRR10002804	Duplicate	320583	0.09415240281978786
+SRR10002804	Ribosomal	11613	0.003410635791499226
+SRR10002804	Unassigned	2166604	0.6363125073973469
+SRR10002804	Bacterial	493004	0.14479093152090627
+SRR10002804	Archaeal	812	2.3847724642188682e-4
+SRR10002804	Viral	233	6.843004731071383e-5
+SRR10002804	Human	187401	0.05503802272993597
+SRR10002805	Filtered	228431	0.05653222369665619
+SRR10002805	Duplicate	384710	0.09520823258813647
+SRR10002805	Ribosomal	13164	0.0032578336247828976
+SRR10002805	Unassigned	2182135	0.5400359143737183
+SRR10002805	Bacterial	637376	0.15773814679653786
+SRR10002805	Archaeal	907	2.2446483573975145e-4
+SRR10002805	Viral	479	1.1854317124513886e-4
+SRR10002805	Human	593520	0.14688464091318335
+SRR10002806	Filtered	248892	0.06168177328035156
+SRR10002806	Duplicate	368765	0.09138935411234127
+SRR10002806	Ribosomal	11259	0.0027902668039289256
+SRR10002806	Unassigned	2019418	0.5004631857764049
+SRR10002806	Bacterial	878621	0.21774464957232761
+SRR10002806	Archaeal	1735	4.299771653625265e-4
+SRR10002806	Viral	341	8.450848033926314e-5
+SRR10002806	Human	506067	0.12541628480894393
+SRR10002807	Filtered	395948	0.08585856066975003
+SRR10002807	Duplicate	444022	0.0962830720844751
+SRR10002807	Ribosomal	14728	0.0031936640203867137
+SRR10002807	Unassigned	2035384	0.44135881643609387
+SRR10002807	Bacterial	1172081	0.25415758546162953
+SRR10002807	Archaeal	1086	2.3549152132943855e-4
+SRR10002807	Viral	556	1.205647199439851e-4
+SRR10002807	Human	547826	0.11879224508639134
+SRR10002808	Filtered	584309	0.1530167108539369
+SRR10002808	Duplicate	219667	0.0575255931761307
+SRR10002808	Ribosomal	12257	0.0032098184777860765
+SRR10002808	Unassigned	1822536	0.4772790837260606
+SRR10002808	Bacterial	609040	0.1594931749784476
+SRR10002808	Archaeal	690	1.8069468464325633e-4
+SRR10002808	Viral	418	1.0946431620417556e-4
+SRR10002808	Human	569679	0.14918545978679074
+SRR10002809	Filtered	362182	0.09239395659411093
+SRR10002809	Duplicate	237170	0.06050293688097501
+SRR10002809	Ribosomal	12553	0.0032023163413031973
+SRR10002809	Unassigned	1929691	0.49227125172992175
+SRR10002809	Bacterial	643844	0.16424696586075166
+SRR10002809	Archaeal	1580	4.0306379504971335e-4
+SRR10002809	Viral	417	1.0637822945299396e-4
+SRR10002809	Human	732538	0.18687313056843474
+SRR10002810	Filtered	333483	0.06316040074712999
+SRR10002810	Duplicate	511006	0.09678257585600437
+SRR10002810	Ribosomal	12806	0.00242540726803989
+SRR10002810	Unassigned	1693034	0.32065414404487325
+SRR10002810	Bacterial	1222760	0.23158605271501295
+SRR10002810	Archaeal	1231	2.3314667710113262e-4
+SRR10002810	Viral	1219	2.3087392314076414e-4
+SRR10002810	Human	1504399	0.28492739876869766
+SRR10002811	Filtered	253661	0.08564345103324773
+SRR10002811	Duplicate	192837	0.06510747086425739
+SRR10002811	Ribosomal	9523	0.0032152462703750996
+SRR10002811	Unassigned	1552300	0.5241023611785433
+SRR10002811	Bacterial	338858	0.11440847639260375
+SRR10002811	Archaeal	580	1.9582514300299882e-4
+SRR10002811	Viral	269	9.082235080656325e-5
+SRR10002811	Human	613798	0.20723634676716324
+SRR10002812	Filtered	1076352	0.7372965819395382
+SRR10002812	Duplicate	32575	0.022313737658944708
+SRR10002812	Ribosomal	1584	0.0010850333216199054
+SRR10002812	Unassigned	55288	0.037872046897551345
+SRR10002812	Bacterial	263525	0.1805135139393217
+SRR10002812	Archaeal	20	1.3699915677019008e-5
+SRR10002812	Viral	213	1.4590410196025245e-4
+SRR10002812	Human	30306	0.020759482225386903
+SRR10002813	Filtered	435894	0.5055560903588118
+SRR10002813	Duplicate	34163	0.03962273560757452
+SRR10002813	Ribosomal	701	8.130298176655953e-4
+SRR10002813	Unassigned	40880	0.04741320819710348
+SRR10002813	Bacterial	319848	0.37096428119929437
+SRR10002813	Archaeal	19	2.2036471520180187e-5
+SRR10002813	Viral	314	3.64181687228241e-4
+SRR10002813	Human	30388	0.035244436660801876
+SRR10002814	Filtered	588862	0.8735049403904548
+SRR10002814	Duplicate	8186	0.01214293237131325
+SRR10002814	Ribosomal	785	0.0011644517360714513
+SRR10002814	Unassigned	21776	0.03230203949642283
+SRR10002814	Bacterial	39044	0.057917010933979295
+SRR10002814	Archaeal	10	1.483378007734333e-5
+SRR10002814	Viral	26	3.856782820109266e-5
+SRR10002814	Human	15448	0.022915223463479975
+SRR10002815	Filtered	645707	0.8601639315872697
+SRR10002815	Duplicate	9172	0.01221827172466527
+SRR10002815	Ribosomal	749	9.977633582396736e-4
+SRR10002815	Unassigned	18934	0.02522249856463282
+SRR10002815	Bacterial	43561	0.0580287979282756
+SRR10002815	Archaeal	6	7.992763884429963e-6
+SRR10002815	Viral	42	5.5949347191009736e-5
+SRR10002815	Human	32508	0.04330479472584154
+SRR10002816	Filtered	1110735	0.8475017911657189
+SRR10002816	Duplicate	17756	0.013548003622770963
+SRR10002816	Ribosomal	1300	9.919128581663804e-4
+SRR10002816	Unassigned	27324	0.020848482258875523
+SRR10002816	Bacterial	126715	0.09668479832504069
+SRR10002816	Archaeal	12	9.156118690766588e-6
+SRR10002816	Viral	103	7.859001876241322e-5
+SRR10002816	Human	26654	0.020337265631974386
+SRR10002817	Filtered	340709	0.7141653373802332
+SRR10002817	Duplicate	8784	0.018412276527910824
+SRR10002817	Ribosomal	474	9.935586377766085e-4
+SRR10002817	Unassigned	12809	0.02684914048793371
+SRR10002817	Bacterial	103183	0.2162834618601346
+SRR10002817	Archaeal	2	4.192230539141809e-6
+SRR10002817	Viral	40	8.384461078283617e-5
+SRR10002817	Human	11072	0.023208188264689053
+SRR10002818	Filtered	411842	0.06097689895242362
+SRR10002818	Duplicate	658827	0.09754524163666746
+SRR10002818	Ribosomal	16139	0.0023895235847562046
+SRR10002818	Unassigned	2207921	0.326902491032809
+SRR10002818	Bacterial	1869467	0.27679134317017334
+SRR10002818	Archaeal	1981	2.9330480335845103e-4
+SRR10002818	Viral	1290	1.909960607432619e-4
+SRR10002818	Human	1586599	0.23491020075906868
+SRR10002819	Filtered	369571	0.05436254691454364
+SRR10002819	Duplicate	694346	0.1021357655225267
+SRR10002819	Ribosomal	17185	0.0025278508560640104
+SRR10002819	Unassigned	2924119	0.43012724570166067
+SRR10002819	Bacterial	1660634	0.24427320794349736
+SRR10002819	Archaeal	2349	3.455293372647286e-4
+SRR10002819	Viral	1040	1.5298020892095263e-4
+SRR10002819	Human	1129021	0.16607487351552197
+SRR10002820	Filtered	412697	0.06145065396210699
+SRR10002820	Duplicate	580920	0.08649908746530068
+SRR10002820	Ribosomal	15565	0.0023176311650440766
+SRR10002820	Unassigned	2047209	0.3048297706237532
+SRR10002820	Bacterial	2116762	0.31518622423263926
+SRR10002820	Archaeal	1926	2.867817297703111e-4
+SRR10002820	Viral	1320	1.965482260108051e-4
+SRR10002820	Human	1539510	0.22923330259537467
+SRR10002821	Filtered	282787	0.05640751395392164
+SRR10002821	Duplicate	434645	0.08669862441520391
+SRR10002821	Ribosomal	9852	0.0019651781286764806
+SRR10002821	Unassigned	1104430	0.22030061719997623
+SRR10002821	Bacterial	1087794	0.21698223480567436
+SRR10002821	Archaeal	1447	2.8863304427475313e-4
+SRR10002821	Viral	765	1.5259452582597522e-4
+SRR10002821	Human	2091566	0.41720460392644665
+SRR10002822	Filtered	393246	0.07297302262437226
+SRR10002822	Duplicate	305456	0.056682197908561695
+SRR10002822	Ribosomal	16030	0.002974620346217602
+SRR10002822	Unassigned	2683008	0.49787462170084823
+SRR10002822	Bacterial	1062615	0.1971850404988158
+SRR10002822	Archaeal	1742	3.232556857835972e-4
+SRR10002822	Viral	906	1.6812264714118202e-4
+SRR10002822	Human	925920	0.17181911858825966
+SRR10002823	Filtered	546529	0.07745579230874323
+SRR10002823	Duplicate	387502	0.05491799050228373
+SRR10002823	Ribosomal	16066	0.0022769235653227345
+SRR10002823	Unassigned	2070612	0.29345358256193443
+SRR10002823	Bacterial	825824	0.11703834970802204
+SRR10002823	Archaeal	643	9.11279629343034e-5
+SRR10002823	Viral	1062	1.5050994811233314e-4
+SRR10002823	Human	3207774	0.4546157234426472
+SRR10002824	Filtered	457611	0.07566633469742214
+SRR10002824	Duplicate	351784	0.05816775795424049
+SRR10002824	Ribosomal	16773	0.0027734285930186586
+SRR10002824	Unassigned	3085180	0.5101369120973771
+SRR10002824	Bacterial	1023003	0.1691543415574952
+SRR10002824	Archaeal	3634	6.008847258707331e-4
+SRR10002824	Viral	1070	1.7692533205329784e-4
+SRR10002824	Human	1108694	0.18332341504252242
+SRR10002825	Filtered	509932	0.07527688241290152
+SRR10002825	Duplicate	396975	0.058602010456024686
+SRR10002825	Ribosomal	19424	0.0028673983275970113
+SRR10002825	Unassigned	3314645	0.4893125787467975
+SRR10002825	Bacterial	1223059	0.1805496978558728
+SRR10002825	Archaeal	1853	2.735424784306663e-4
+SRR10002825	Viral	1148	1.6946938221176734e-4
+SRR10002825	Human	1307049	0.19294842034016402
+SRR10002826	Filtered	519934	0.08435595463150264
+SRR10002826	Duplicate	348527	0.05654626894923918
+SRR10002826	Ribosomal	13750	0.002230849254296048
+SRR10002826	Unassigned	2999573	0.4866614683822952
+SRR10002826	Bacterial	1187719	0.19269978512459982
+SRR10002826	Archaeal	2949	4.784563237032033e-4
+SRR10002826	Viral	734	1.190867892838763e-4
+SRR10002826	Human	1090386	0.17690813054508003
+SRR10002827	Filtered	485863	0.07335290264246062
+SRR10002827	Duplicate	422417	0.06377417723827558
+SRR10002827	Ribosomal	16981	0.002563697255752391
+SRR10002827	Unassigned	3956809	0.597377090562179
+SRR10002827	Bacterial	1096792	0.165587576734655
+SRR10002827	Archaeal	2235	3.3742791158392283e-4
+SRR10002827	Viral	692	1.0447432430249423e-4
+SRR10002827	Human	641848	0.09690265333079093
+SRR10002828	Filtered	457571	0.0701953198108779
+SRR10002828	Duplicate	408330	0.06264132765926113
+SRR10002828	Ribosomal	19409	0.0029775072332148
+SRR10002828	Unassigned	3796105	0.5823550979207001
+SRR10002828	Bacterial	1395159	0.21402936853958096
+SRR10002828	Archaeal	4704	7.216339855243658e-4
+SRR10002828	Viral	552	8.468153911765518e-5
+SRR10002828	Human	436710	0.06699506331172318
+SRR10002829	Filtered	314732	0.06469544125431027
+SRR10002829	Duplicate	266491	0.05477915444029333
+SRR10002829	Ribosomal	12951	0.0026621718150190397
+SRR10002829	Unassigned	2056131	0.42265261340335986
+SRR10002829	Bacterial	800949	0.1646408658070948
+SRR10002829	Archaeal	899	1.8479595874466194e-4
+SRR10002829	Viral	788	1.6197910510655573e-4
+SRR10002829	Human	1411884	0.2902229782160715
+SRR10002830	Filtered	308664	0.07253496865851292
+SRR10002830	Duplicate	265912	0.06248839704637563
+SRR10002830	Ribosomal	19520	0.0045871322480566966
+SRR10002830	Unassigned	2185939	0.5136880778270905
+SRR10002830	Bacterial	613218	0.14410410158241962
+SRR10002830	Archaeal	604	1.4193790357716417e-4
+SRR10002830	Viral	544	1.2783811183108825e-4
+SRR10002830	Human	860981	0.2023275466221364
+SRR10002831	Filtered	282456	0.07073759626507416
+SRR10002831	Duplicate	238660	0.05976943214030715
+SRR10002831	Ribosomal	19467	0.004875268312559119
+SRR10002831	Unassigned	2838577	0.7108863461683427
+SRR10002831	Bacterial	511775	0.1281676909980964
+SRR10002831	Archaeal	1246	3.1204522101241393e-4
+SRR10002831	Viral	581	1.4550423226983346e-4
+SRR10002831	Human	100249	0.025106116662338272
+SRR10002832	Filtered	412309	0.08262851128436734
+SRR10002832	Duplicate	262315	0.05256906334219922
+SRR10002832	Ribosomal	12631	0.0025313071653367833
+SRR10002832	Unassigned	2396836	0.48033632657249264
+SRR10002832	Bacterial	1108062	0.22206042912179613
+SRR10002832	Archaeal	1723	3.4529667056252693e-4
+SRR10002832	Viral	751	1.505036561767021e-4
+SRR10002832	Human	795285	0.15937856218706864
+SRR10002833	Filtered	424908	0.08140575454471781
+SRR10002833	Duplicate	280586	0.05375590726624162
+SRR10002833	Ribosomal	14340	0.0027473206439305765
+SRR10002833	Unassigned	2295194	0.43972342106175705
+SRR10002833	Bacterial	919726	0.17620517618965786
+SRR10002833	Archaeal	1655	3.170722221551677e-4
+SRR10002833	Viral	1002	1.9196759311146709e-4
+SRR10002833	Human	1282220	0.24565338047842847
+SRR10002834	Filtered	508408	0.12187046648519023
+SRR10002834	Duplicate	179085	0.04292845999767961
+SRR10002834	Ribosomal	22271	0.005338580744385753
+SRR10002834	Unassigned	2894539	0.6938498571807998
+SRR10002834	Bacterial	530850	0.1272500376344653
+SRR10002834	Archaeal	1689	4.04870139520791e-4
+SRR10002834	Viral	199	4.770228405247922e-5
+SRR10002834	Human	34667	0.008310025533906017
+SRR10002835	Filtered	1124694	0.17414442405067015
+SRR10002835	Duplicate	381806	0.05911775644672255
+SRR10002835	Ribosomal	20672	0.0032007937572134763
+SRR10002835	Unassigned	1965292	0.30430023049059535
+SRR10002835	Bacterial	665492	0.10304289082215125
+SRR10002835	Archaeal	753	1.1659238095886936e-4
+SRR10002835	Viral	820	1.269664706325005e-4
+SRR10002835	Human	2298869	0.35595034558105587
+SRR10002836	Filtered	961536	0.1806800078620577
+SRR10002836	Duplicate	325284	0.06112336478031148
+SRR10002836	Ribosomal	17574	0.003302289730356224
+SRR10002836	Unassigned	1607604	0.3020811528211897
+SRR10002836	Bacterial	722316	0.13572873044679562
+SRR10002836	Archaeal	565	1.061678444094268e-4
+SRR10002836	Viral	704	1.32287013211038e-4
+SRR10002836	Human	1686179	0.3168459995016688
+SRR10002837	Filtered	368188	0.08703360334643528
+SRR10002837	Duplicate	213523	0.05047333451209953
+SRR10002837	Ribosomal	23378	0.0055261757010901065
+SRR10002837	Unassigned	3007249	0.7108643318901327
+SRR10002837	Bacterial	529568	0.1251811880261308
+SRR10002837	Archaeal	1541	3.6426712102745546e-4
+SRR10002837	Viral	383	9.053491716646038e-5
+SRR10002837	Human	86582	0.020466564485917684
+SRR10002838	Filtered	354560	0.07305105167471844
+SRR10002838	Duplicate	421150	0.08677078749099636
+SRR10002838	Ribosomal	13742	0.002831305144725803
+SRR10002838	Unassigned	1844624	0.3800533707818869
+SRR10002838	Bacterial	703657	0.14497654520610714
+SRR10002838	Archaeal	513	1.0569491625995758e-4
+SRR10002838	Viral	888	1.8295728194706105e-4
+SRR10002838	Human	1514458	0.3120282875033583
+SRR10002839	Filtered	260109	0.06356826301149175
+SRR10002839	Duplicate	333170	0.08142370386083797
+SRR10002839	Ribosomal	3865	9.445706859025086e-4
+SRR10002839	Unassigned	223720	0.054675123893948054
+SRR10002839	Bacterial	82208	0.020090884074171648
+SRR10002839	Archaeal	66	1.612979696495875e-5
+SRR10002839	Viral	59	1.44190609232207e-5
+SRR10002839	Human	3188609	0.7792669056157598
+SRR10002840	Filtered	429205	0.0845144610198327
+SRR10002840	Duplicate	371403	0.07313270899954318
+SRR10002840	Ribosomal	34880	0.006868196783289488
+SRR10002840	Unassigned	3527016	0.6945022920243853
+SRR10002840	Bacterial	425923	0.08386820465966195
+SRR10002840	Archaeal	460	9.057828326585908e-5
+SRR10002840	Viral	729	1.4354688804524187e-4
+SRR10002840	Human	288864	0.05688001134197634
+SRR10002841	Filtered	496384	0.08255033545885679
+SRR10002841	Duplicate	664065	0.11043625200748963
+SRR10002841	Ribosomal	15313	0.0025466036110782663
+SRR10002841	Unassigned	2437731	0.40540289737069374
+SRR10002841	Bacterial	1329435	0.22108952992188563
+SRR10002841	Archaeal	1276	2.1220310897511054e-4
+SRR10002841	Viral	1678	2.7905706650488676e-4
+SRR10002841	Human	1067225	0.17748312145451595
+SRR10002842	Filtered	873846	0.3322082894872902
+SRR10002842	Duplicate	179647	0.06829604138660957
+SRR10002842	Ribosomal	7140	0.002714399547448008
+SRR10002842	Unassigned	1000361	0.3803052444936466
+SRR10002842	Bacterial	250897	0.09538301166051301
+SRR10002842	Archaeal	679	2.5813407461025177e-4
+SRR10002842	Viral	195	7.413276074963047e-5
+SRR10002842	Human	317651	0.12076074658913268
+SRR10002843	Filtered	608452	0.21145050503646376
+SRR10002843	Duplicate	222240	0.07723330721125693
+SRR10002843	Ribosomal	7493	0.0026039829505667216
+SRR10002843	Unassigned	1203405	0.41820980950577147
+SRR10002843	Bacterial	279868	0.0972603096769261
+SRR10002843	Archaeal	317	1.1016449957689187e-4
+SRR10002843	Viral	235	8.166768896078735e-5
+SRR10002843	Human	555505	0.19305025343047733
+SRR10002844	Filtered	387751	0.10652154682897642
+SRR10002844	Duplicate	334131	0.09179125511865274
+SRR10002844	Ribosomal	8442	0.002319155587813362
+SRR10002844	Unassigned	1418312	0.3896335228693136
+SRR10002844	Bacterial	434847	0.11945958894739127
+SRR10002844	Archaeal	415	1.1400729317016646e-4
+SRR10002844	Viral	550	1.5109400299660616e-4
+SRR10002844	Human	1055670	0.29000982935168584
+SRR10002845	Filtered	341543	0.18184165635561728
+SRR10002845	Duplicate	148505	0.07906587216570371
+SRR10002845	Ribosomal	4888	0.0026024307810912748
+SRR10002845	Unassigned	853111	0.45420669518976237
+SRR10002845	Bacterial	206685	0.11004161333671238
+SRR10002845	Archaeal	1107	5.893802935081917e-4
+SRR10002845	Viral	138	7.347288211755235e-5
+SRR10002845	Human	322267	0.17157887899548727
+SRR10002846	Filtered	256889	0.05736034647222113
+SRR10002846	Duplicate	426704	0.09527807450331717
+SRR10002846	Ribosomal	9581	0.0021393266334889804
+SRR10002846	Unassigned	1326994	0.2963024325936829
+SRR10002846	Bacterial	1179550	0.26337989046361826
+SRR10002846	Archaeal	1002	2.2373502627658472e-4
+SRR10002846	Viral	920	2.0542537342760274e-4
+SRR10002846	Human	1276872	0.28511076893396736
+SRR10002847	Filtered	215831	0.053109014833938824
+SRR10002847	Duplicate	385264	0.09480098545149959
+SRR10002847	Ribosomal	10634	0.0026166827922963127
+SRR10002847	Unassigned	1097101	0.26996100320773714
+SRR10002847	Bacterial	852878	0.20986563725109034
+SRR10002847	Archaeal	798	1.9636193983942613e-4
+SRR10002847	Viral	957	2.3548668725104113e-4
+SRR10002847	Human	1500461	0.36921482783634735
+SRR10002848	Filtered	518850	0.10544370797671439
+SRR10002848	Duplicate	314677	0.06395048606531474
+SRR10002848	Ribosomal	12061	0.002451106412078929
+SRR10002848	Unassigned	1988754	0.40416612896506243
+SRR10002848	Bacterial	785516	0.1596371200058529
+SRR10002848	Archaeal	802	1.6298709414536944e-4
+SRR10002848	Viral	727	1.4774515890733614e-4
+SRR10002848	Human	1299248	0.2640407183219239
+SRR10002849	Filtered	373876	0.10631184268818177
+SRR10002849	Duplicate	212125	0.060317858408217045
+SRR10002849	Ribosomal	8625	0.0024525234120017537
+SRR10002849	Unassigned	1461762	0.41565281481443567
+SRR10002849	Bacterial	412775	0.11737279436394481
+SRR10002849	Archaeal	617	1.7544428350203852e-4
+SRR10002849	Viral	401	1.1402456674929893e-4
+SRR10002849	Human	1046605	0.2976026974629676
+SRR10002850	Filtered	557590	0.21156270085726925
+SRR10002850	Duplicate	139538	0.05294398420384447
+SRR10002850	Ribosomal	7654	0.0029041068031376797
+SRR10002850	Unassigned	1440135	0.5464209368874683
+SRR10002850	Bacterial	207745	0.07882331693465343
+SRR10002850	Archaeal	431	1.6353149100500916e-4
+SRR10002850	Viral	110	4.173657543051278e-5
+SRR10002850	Human	282375	0.10713968624719132
+SRR10002851	Filtered	717455	0.15762446028086816
+SRR10002851	Duplicate	249914	0.05490596534505005
+SRR10002851	Ribosomal	15610	0.0034295082269750047
+SRR10002851	Unassigned	2570973	0.5648413231794112
+SRR10002851	Bacterial	560171	0.12306925387654166
+SRR10002851	Archaeal	1118	2.456239716693181e-4
+SRR10002851	Viral	226	4.96520729850321e-5
+SRR10002851	Human	436206	0.09583421304649961
+SRR10002852	Filtered	535985	0.11773004842662346
+SRR10002852	Duplicate	262665	0.05769482946347202
+SRR10002852	Ribosomal	14382	0.0031590316081078736
+SRR10002852	Unassigned	2183704	0.47965442627948796
+SRR10002852	Bacterial	689349	0.151416720902347
+SRR10002852	Archaeal	1743	3.828530171695191e-4
+SRR10002852	Viral	465	1.0213806826381318e-4
+SRR10002852	Human	864368	0.18985995223452834
+SRR10002853	Filtered	517300	0.11026636799039598
+SRR10002853	Duplicate	264989	0.056484389304865726
+SRR10002853	Ribosomal	13364	0.0028486366553714514
+SRR10002853	Unassigned	2439734	0.5200475682247839
+SRR10002853	Bacterial	669892	0.14279249523646306
+SRR10002853	Archaeal	2381	5.075279763872662e-4
+SRR10002853	Viral	535	1.1403925550910854e-4
+SRR10002853	Human	783172	0.16693897535622346
+SRR10002854	Filtered	508837	0.12048911994639003
+SRR10002854	Duplicate	225478	0.053391647594951096
+SRR10002854	Ribosomal	19643	0.00465132799522625
+SRR10002854	Unassigned	2255073	0.5339858563446951
+SRR10002854	Bacterial	799890	0.18940847885259507
+SRR10002854	Archaeal	1221	2.89124445460024e-4
+SRR10002854	Viral	298	7.056436097222535e-5
+SRR10002854	Human	412655	0.09771388045971023
+SRR10002855	Filtered	477609	0.11461162724566525
+SRR10002855	Duplicate	264137	0.06338484280193271
+SRR10002855	Ribosomal	15724	0.0037732815478997263
+SRR10002855	Unassigned	2293239	0.5503075809987293
+SRR10002855	Bacterial	582190	0.1397078850401769
+SRR10002855	Archaeal	1454	3.489157574819513e-4
+SRR10002855	Viral	269	6.455181482987957e-5
+SRR10002855	Human	532573	0.12780131479328422
+SRR10002856	Filtered	442497	0.11350355133858146
+SRR10002856	Duplicate	250207	0.06417983188535166
+SRR10002856	Ribosomal	14518	0.003723967751947529
+SRR10002856	Unassigned	2654314	0.6808499613957055
+SRR10002856	Bacterial	382508	0.09811595652720384
+SRR10002856	Archaeal	635	1.6288190676998766e-4
+SRR10002856	Viral	268	6.874385986512865e-5
+SRR10002856	Human	153583	0.039395105334574826
+SRR10002857	Filtered	420584	0.09903170806997848
+SRR10002857	Duplicate	243389	0.057308952303092825
+SRR10002857	Ribosomal	12221	0.0028775857006524425
+SRR10002857	Unassigned	2446199	0.5759878294206943
+SRR10002857	Bacterial	552796	0.1301626597641656
+SRR10002857	Archaeal	908	2.1379983767223779e-4
+SRR10002857	Viral	368	8.665015447509197e-5
+SRR10002857	Human	570498	0.13433081474926906
+SRR10002858	Filtered	258735	0.07291507852880827
+SRR10002858	Duplicate	304042	0.08568321373627813
+SRR10002858	Ribosomal	6500	0.0018317893228100325
+SRR10002858	Unassigned	758549	0.2137695321581888
+SRR10002858	Bacterial	724888	0.20428339978971058
+SRR10002858	Archaeal	1106	3.116859986196763e-4
+SRR10002858	Viral	533	1.5020672447042265e-4
+SRR10002858	Human	1494090	0.42105509374111405
+SRR10002859	Filtered	312555	0.09802119833270977
+SRR10002859	Duplicate	181548	0.05693574735616705
+SRR10002859	Ribosomal	8855	0.002777039916930284
+SRR10002859	Unassigned	878691	0.27556860323516524
+SRR10002859	Bacterial	433653	0.13599906167098458
+SRR10002859	Archaeal	287	9.000682734714756e-5
+SRR10002859	Viral	363	1.1384138789900543e-4
+SRR10002859	Human	1372695	0.43049450127279687
+SRR10002860	Filtered	404273	0.07558807636719225
+SRR10002860	Duplicate	273470	0.05113146622241917
+SRR10002860	Ribosomal	14809	0.0027688809861696182
+SRR10002860	Unassigned	2813212	0.5259942748912285
+SRR10002860	Bacterial	942610	0.1762424813541322
+SRR10002860	Archaeal	1372	2.565267548804589e-4
+SRR10002860	Viral	729	1.3630321013692023e-4
+SRR10002860	Human	897895	0.16788199021384084
+SRR10002861	Filtered	468962	0.08708400555005898
+SRR10002861	Duplicate	352729	0.06550009210483312
+SRR10002861	Ribosomal	16055	0.002981336886797218
+SRR10002861	Unassigned	2703709	0.5020658594123711
+SRR10002861	Bacterial	917483	0.17037221494296928
+SRR10002861	Archaeal	1213	2.2524831165898632e-4
+SRR10002861	Viral	690	1.281297073740318e-4
+SRR10002861	Human	924327	0.1716431130839372
+SRR10002862	Filtered	538226	0.06877303184719886
+SRR10002862	Duplicate	456851	0.05837515908266165
+SRR10002862	Ribosomal	20212	0.00258263353999172
+SRR10002862	Unassigned	3970247	0.5073071969251686
+SRR10002862	Bacterial	1605422	0.20513638942413354
+SRR10002862	Archaeal	1520	1.9422140217630193e-4
+SRR10002862	Viral	1339	1.7109372204872914e-4
+SRR10002862	Human	1232303	0.15746027405662064
+SRR10002863	Filtered	406530	0.07873029128251767
+SRR10002863	Duplicate	260696	0.0504874720591032
+SRR10002863	Ribosomal	11571	0.0022408880044031483
+SRR10002863	Unassigned	2065991	0.4001084131972055
+SRR10002863	Bacterial	699530	0.1354738903915076
+SRR10002863	Archaeal	3743	7.248849538052878e-4
+SRR10002863	Viral	365	7.068741868526049e-5
+SRR10002863	Human	1715152	0.33216347269277235
+SRR10002864	Filtered	469628	0.06714884363888429
+SRR10002864	Duplicate	425994	0.06090992125065558
+SRR10002864	Ribosomal	21801	0.0031171734653200334
+SRR10002864	Unassigned	3985827	0.5699056998190979
+SRR10002864	Bacterial	1489527	0.21297711298920935
+SRR10002864	Archaeal	5693	8.140025016314366e-4
+SRR10002864	Viral	662	9.465477886527508e-5
+SRR10002864	Human	594704	0.08503259155633618
+SRR10002865	Filtered	367763	0.07060842632580754
+SRR10002865	Duplicate	274898	0.052778868945793464
+SRR10002865	Ribosomal	17297	0.00332092665699783
+SRR10002865	Unassigned	2680587	0.5146576183558907
+SRR10002865	Bacterial	1057490	0.20303212872224288
+SRR10002865	Archaeal	2412	4.630904258934362e-4
+SRR10002865	Viral	785	1.5071558222485383e-4
+SRR10002865	Human	807254	0.15498822498514925
+SRR10002866	Filtered	254879	0.07326648257988257
+SRR10002866	Duplicate	152690	0.043891647507728254
+SRR10002866	Ribosomal	9370	0.0026934621595874893
+SRR10002866	Unassigned	1338208	0.38467583881080625
+SRR10002866	Bacterial	692312	0.19900919686535046
+SRR10002866	Archaeal	646	1.8569653736323565e-4
+SRR10002866	Viral	875	2.5152394766692136e-4
+SRR10002866	Human	1029814	0.2960261515916148
+SRR10002867	Filtered	313414	0.08945300815829792
+SRR10002867	Duplicate	151400	0.043211807497962135
+SRR10002867	Ribosomal	9999	0.0028538630328409737
+SRR10002867	Unassigned	1610162	0.45956413728225703
+SRR10002867	Bacterial	744184	0.21240116083925664
+SRR10002867	Archaeal	969	2.7656698458074843e-4
+SRR10002867	Viral	636	1.8152384127281322e-4
+SRR10002867	Human	672908	0.19205793236353175
+SRR10002868	Filtered	393403	0.08700801533664923
+SRR10002868	Duplicate	253353	0.05603348655090604
+SRR10002868	Ribosomal	12487	0.002761720392342557
+SRR10002868	Unassigned	2293467	0.5072406969700254
+SRR10002868	Bacterial	900264	0.19910926942355087
+SRR10002868	Archaeal	1570	3.472332038101877e-4
+SRR10002868	Viral	624	1.380086109411192e-4
+SRR10002868	Human	666289	0.14736156951177462
+SRR10002869	Filtered	460920	0.1017919373859887
+SRR10002869	Duplicate	235143	0.05193018643745887
+SRR10002869	Ribosomal	12039	0.00265875452180404
+SRR10002869	Unassigned	2384609	0.5266292849476376
+SRR10002869	Bacterial	805148	0.17781301484520964
+SRR10002869	Archaeal	3391	7.488858363184234e-4
+SRR10002869	Viral	557	1.230107374902276e-4
+SRR10002869	Human	626253	0.13830492528809246
+SRR10002870	Filtered	343499	0.061443912902865744
+SRR10002870	Duplicate	525797	0.09405274854537597
+SRR10002870	Ribosomal	19093	0.003415289794306288
+SRR10002870	Unassigned	1704152	0.30483281482986696
+SRR10002870	Bacterial	1257169	0.22487804197445357
+SRR10002870	Archaeal	9400	0.0016814394839197145
+SRR10002870	Viral	1326	2.3719029315718526e-4
+SRR10002870	Human	1730012	0.3094585621760546
+SRR10002871	Filtered	265967	0.0824282661139362
+SRR10002871	Duplicate	185477	0.05748287386786535
+SRR10002871	Ribosomal	12578	0.00389816304722424
+SRR10002871	Unassigned	2236516	0.6931391338627579
+SRR10002871	Bacterial	376235	0.11660243075786389
+SRR10002871	Archaeal	832	2.5785273137943774e-4
+SRR10002871	Viral	156	4.8347387133644576e-5
+SRR10002871	Human	148887	0.04614293223183936
+SRR10002872	Filtered	291962	0.07842470463346184
+SRR10002872	Duplicate	217706	0.05847859908800612
+SRR10002872	Ribosomal	14401	0.0038682916661294414
+SRR10002872	Unassigned	2387232	0.6412408617955363
+SRR10002872	Bacterial	534579	0.14359471499116802
+SRR10002872	Archaeal	1097	2.9466814511103375e-4
+SRR10002872	Viral	278	7.467433394791921e-5
+SRR10002872	Human	275577	0.07402348534663933
+SRR10002873	Filtered	179035	0.053335148558504665
+SRR10002873	Duplicate	235026	0.07001506199967111
+SRR10002873	Ribosomal	6153	0.0018330000786465172
+SRR10002873	Unassigned	549609	0.16373043072075957
+SRR10002873	Bacterial	258417	0.07698332217188315
+SRR10002873	Archaeal	197	5.868698447803737e-5
+SRR10002873	Viral	292	8.698781455627874e-5
+SRR10002873	Human	2128063	0.6339573616715006
+SRR10002874	Filtered	287495	0.07708777433669844
+SRR10002874	Duplicate	220277	0.05906420517770717
+SRR10002874	Ribosomal	12588	0.0033752966255077826
+SRR10002874	Unassigned	2494295	0.6688104144042687
+SRR10002874	Bacterial	654638	0.17553204896164315
+SRR10002874	Archaeal	1283	3.440185550148145e-4
+SRR10002874	Viral	201	5.38953464988135e-5
+SRR10002874	Human	58673	0.015732346592661116
diff --git a/data/2024-04-12_leung/3/adapters.fasta b/data/2024-04-12_leung/3/adapters.fasta
new file mode 100644
index 0000000..686a505
--- /dev/null
+++ b/data/2024-04-12_leung/3/adapters.fasta
@@ -0,0 +1,41 @@
+>0
+CTGTCTCTTATACACATCTGACGCTGCCGACGA
+>1
+AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
+>2
+TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG
+>3
+TGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
+>4
+CTGTCTCTTATACACATCTCCGAGCCCACGAGAC
+>5
+ACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>6
+GATCGGAAGAGCGGTTCAGCAGGAATGCCGAG
+>7
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
+>8
+CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT
+>9
+AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>10
+CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT
+>11
+CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATC
+T
+>12
+unspecified
+>13
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGATC
+>14
+CAAGCAGAAGACGGCATACGAGAT
+>15
+GATCGGAAGAGCACACGTCTGAACTCCAGTCAC
+>16
+GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG
+>17
+heifigepsna
+>18
+GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG
+>19
+AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
diff --git a/data/2024-04-12_leung/3/bracken_counts.tsv b/data/2024-04-12_leung/3/bracken_counts.tsv
new file mode 100644
index 0000000..02a1a90
--- /dev/null
+++ b/data/2024-04-12_leung/3/bracken_counts.tsv
@@ -0,0 +1,373 @@
+name	taxonomy_id	taxonomy_lvl	kraken_assigned_reads	added_reads	new_est_reads	fraction_total_reads	sample
+Bacteria	2	D	440771	300	441071	0.88255	SRR10002879
+Eukaryota	2759	D	57232	393	57625	0.1153	SRR10002879
+Archaea	2157	D	775	1	776	0.00155	SRR10002879
+Viruses	10239	D	297	1	298	6e-4	SRR10002879
+Eukaryota	2759	D	893040	992	894032	0.5822	SRR10002886
+Bacteria	2	D	640190	212	640402	0.41704	SRR10002886
+Archaea	2157	D	637	0	637	4.1e-4	SRR10002886
+Viruses	10239	D	503	32	535	3.5e-4	SRR10002886
+Bacteria	2	D	701099	319	701418	0.51216	SRR10002938
+Eukaryota	2759	D	645329	1399	646728	0.47222	SRR10002938
+Archaea	2157	D	20967	9	20976	0.01532	SRR10002938
+Viruses	10239	D	386	29	415	3e-4	SRR10002938
+Eukaryota	2759	D	2042633	631	2043264	0.79601	SRR10002891
+Bacteria	2	D	522309	77	522386	0.20351	SRR10002891
+Archaea	2157	D	838	0	838	3.3e-4	SRR10002891
+Viruses	10239	D	359	27	386	1.5e-4	SRR10002891
+Eukaryota	2759	D	700913	612	701525	0.5893	SRR10002966
+Bacteria	2	D	487325	352	487677	0.40967	SRR10002966
+Archaea	2157	D	588	0	588	4.9e-4	SRR10002966
+Viruses	10239	D	611	27	638	5.4e-4	SRR10002966
+Bacteria	2	D	1325931	556	1326487	0.59184	SRR10002939
+Eukaryota	2759	D	911519	1363	912882	0.4073	SRR10002939
+Archaea	2157	D	1252	0	1252	5.6e-4	SRR10002939
+Viruses	10239	D	663	16	679	3e-4	SRR10002939
+Bacteria	2	D	1564240	338	1564578	0.63339	SRR10002945
+Eukaryota	2759	D	900786	910	901696	0.36503	SRR10002945
+Archaea	2157	D	2901	0	2901	0.00117	SRR10002945
+Viruses	10239	D	985	6	991	4e-4	SRR10002945
+Eukaryota	2759	D	863452	1001	864453	0.6407	SRR10002894
+Bacteria	2	D	483481	181	483662	0.35847	SRR10002894
+Archaea	2157	D	593	0	593	4.4e-4	SRR10002894
+Viruses	10239	D	503	15	518	3.8e-4	SRR10002894
+Bacteria	2	D	1405923	414	1406337	0.71129	SRR10002911
+Eukaryota	2759	D	565677	1803	567480	0.28702	SRR10002911
+Archaea	2157	D	2944	2	2946	0.00149	SRR10002911
+Viruses	10239	D	364	43	407	2.1e-4	SRR10002911
+Bacteria	2	D	1499966	358	1500324	0.69893	SRR10002955
+Eukaryota	2759	D	642060	1403	643463	0.29976	SRR10002955
+Archaea	2157	D	2141	1	2142	0.001	SRR10002955
+Viruses	10239	D	658	9	667	3.1e-4	SRR10002955
+Bacteria	2	D	774010	462	774472	0.5527	SRR10002909
+Eukaryota	2759	D	624316	836	625152	0.44614	SRR10002909
+Archaea	2157	D	1162	0	1162	8.3e-4	SRR10002909
+Viruses	10239	D	428	36	464	3.3e-4	SRR10002909
+Eukaryota	2759	D	1014367	555	1014922	0.56598	SRR10002896
+Bacteria	2	D	776900	474	777374	0.43351	SRR10002896
+Archaea	2157	D	460	0	460	2.6e-4	SRR10002896
+Viruses	10239	D	414	40	454	2.5e-4	SRR10002896
+Bacteria	2	D	1217832	396	1218228	0.73002	SRR10002907
+Eukaryota	2759	D	447721	906	448627	0.26884	SRR10002907
+Archaea	2157	D	1128	0	1128	6.8e-4	SRR10002907
+Viruses	10239	D	750	16	766	4.6e-4	SRR10002907
+Eukaryota	2759	D	1748056	678	1748734	0.7776	SRR10002913
+Bacteria	2	D	499145	139	499284	0.22201	SRR10002913
+Archaea	2157	D	432	0	432	1.9e-4	SRR10002913
+Viruses	10239	D	424	1	425	1.9e-4	SRR10002913
+Eukaryota	2759	D	767421	721	768142	0.58801	SRR10002958
+Bacteria	2	D	536916	134	537050	0.41111	SRR10002958
+Archaea	2157	D	777	0	777	5.9e-4	SRR10002958
+Viruses	10239	D	369	4	373	2.9e-4	SRR10002958
+Eukaryota	2759	D	1030481	781	1031262	0.71634	SRR10002962
+Bacteria	2	D	406668	111	406779	0.28256	SRR10002962
+Archaea	2157	D	1351	0	1351	9.4e-4	SRR10002962
+Viruses	10239	D	231	9	240	1.7e-4	SRR10002962
+Bacteria	2	D	897579	287	897866	0.69108	SRR10002905
+Eukaryota	2759	D	398928	783	399711	0.30765	SRR10002905
+Archaea	2157	D	1259	0	1259	9.7e-4	SRR10002905
+Viruses	10239	D	378	5	383	2.9e-4	SRR10002905
+Eukaryota	2759	D	1639045	1546	1640591	0.6823	SRR10002927
+Bacteria	2	D	761996	259	762255	0.31701	SRR10002927
+Archaea	2157	D	831	0	831	3.5e-4	SRR10002927
+Viruses	10239	D	807	22	829	3.4e-4	SRR10002927
+Eukaryota	2759	D	455648	635	456283	0.52109	SRR10002937
+Bacteria	2	D	416967	183	417150	0.4764	SRR10002937
+Archaea	2157	D	1832	0	1832	0.00209	SRR10002937
+Viruses	10239	D	331	35	366	4.2e-4	SRR10002937
+Bacteria	2	D	1688795	598	1689393	0.93543	SRR10002908
+Eukaryota	2759	D	113158	980	114138	0.0632	SRR10002908
+Archaea	2157	D	2045	3	2048	0.00113	SRR10002908
+Viruses	10239	D	356	70	426	2.4e-4	SRR10002908
+Eukaryota	2759	D	1811280	926	1812206	0.58898	SRR10002895
+Bacteria	2	D	1262208	231	1262439	0.4103	SRR10002895
+Archaea	2157	D	1235	0	1235	4e-4	SRR10002895
+Viruses	10239	D	990	5	995	3.2e-4	SRR10002895
+Bacteria	2	D	618893	228	619121	0.51652	SRR10002917
+Eukaryota	2759	D	576792	1007	577799	0.48204	SRR10002917
+Archaea	2157	D	1291	0	1291	0.00108	SRR10002917
+Viruses	10239	D	419	17	436	3.6e-4	SRR10002917
+Eukaryota	2759	D	885248	911	886159	0.6275	SRR10002960
+Bacteria	2	D	524032	217	524249	0.37123	SRR10002960
+Archaea	2157	D	1337	0	1337	9.5e-4	SRR10002960
+Viruses	10239	D	420	32	452	3.2e-4	SRR10002960
+Eukaryota	2759	D	864153	1192	865345	0.50394	SRR10002885
+Bacteria	2	D	849988	189	850177	0.49511	SRR10002885
+Archaea	2157	D	1087	0	1087	6.3e-4	SRR10002885
+Viruses	10239	D	517	34	551	3.2e-4	SRR10002885
+Eukaryota	2759	D	701305	821	702126	0.51021	SRR10002943
+Bacteria	2	D	672280	272	672552	0.48872	SRR10002943
+Archaea	2157	D	871	0	871	6.3e-4	SRR10002943
+Viruses	10239	D	602	9	611	4.4e-4	SRR10002943
+Eukaryota	2759	D	1851432	1087	1852519	0.58778	SRR10002892
+Bacteria	2	D	1295627	334	1295961	0.41119	SRR10002892
+Archaea	2157	D	1742	0	1742	5.5e-4	SRR10002892
+Viruses	10239	D	1511	5	1516	4.8e-4	SRR10002892
+Eukaryota	2759	D	1711727	764	1712491	0.67187	SRR10002952
+Bacteria	2	D	835136	161	835297	0.32772	SRR10002952
+Archaea	2157	D	620	0	620	2.4e-4	SRR10002952
+Viruses	10239	D	413	6	419	1.6e-4	SRR10002952
+Eukaryota	2759	D	1307444	656	1308100	0.698	SRR10002880
+Bacteria	2	D	564725	197	564922	0.30144	SRR10002880
+Archaea	2157	D	473	0	473	2.5e-4	SRR10002880
+Viruses	10239	D	559	9	568	3e-4	SRR10002880
+Bacteria	2	D	698557	299	698856	0.55298	SRR10002918
+Eukaryota	2759	D	560990	1001	561991	0.44468	SRR10002918
+Archaea	2157	D	2532	0	2532	0.002	SRR10002918
+Viruses	10239	D	353	71	424	3.4e-4	SRR10002918
+Bacteria	2	D	915769	8467	924236	0.98602	SRR10002934
+Eukaryota	2759	D	12496	9	12505	0.01334	SRR10002934
+Archaea	2157	D	18	0	18	2e-5	SRR10002934
+Viruses	10239	D	16	568	584	6.2e-4	SRR10002934
+Eukaryota	2759	D	1951313	1291	1952604	0.53388	SRR10002925
+Bacteria	2	D	1700269	497	1700766	0.46503	SRR10002925
+Archaea	2157	D	1666	0	1666	4.6e-4	SRR10002925
+Viruses	10239	D	2307	14	2321	6.3e-4	SRR10002925
+Bacteria	2	D	1217828	313	1218141	0.72434	SRR10002898
+Eukaryota	2759	D	459891	1429	461320	0.27432	SRR10002898
+Archaea	2157	D	1915	1	1916	0.00114	SRR10002898
+Viruses	10239	D	327	10	337	2e-4	SRR10002898
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+Eukaryota	2759	D	795390	1767	797157	0.31871	SRR10002889
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+Viruses	10239	D	498	21	519	2.1e-4	SRR10002889
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+Eukaryota	2759	D	1100466	973	1101439	0.43649	SRR10002946
+Archaea	2157	D	1750	0	1750	6.9e-4	SRR10002946
+Viruses	10239	D	991	14	1005	4e-4	SRR10002946
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+Archaea	2157	D	774	0	774	2.8e-4	SRR10002932
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+Eukaryota	2759	D	704128	767	704895	0.56567	SRR10002963
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+Viruses	10239	D	473	13	486	3.9e-4	SRR10002963
+Eukaryota	2759	D	797857	569	798426	0.62332	SRR10002877
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+Archaea	2157	D	383	0	383	3e-4	SRR10002877
+Viruses	10239	D	498	4	502	3.9e-4	SRR10002877
+Eukaryota	2759	D	1001050	766	1001816	0.68095	SRR10002959
+Bacteria	2	D	468173	170	468343	0.31834	SRR10002959
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+Viruses	10239	D	320	3	323	2.2e-4	SRR10002959
+Eukaryota	2759	D	594865	712	595577	0.60786	SRR10002926
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+Archaea	2157	D	794	0	794	8.1e-4	SRR10002926
+Viruses	10239	D	312	7	319	3.3e-4	SRR10002926
+Bacteria	2	D	945092	183	945275	0.52225	SRR10002876
+Eukaryota	2759	D	862262	886	863148	0.47687	SRR10002876
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+Viruses	10239	D	439	9	448	1.7e-4	SRR10002950
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+Eukaryota	2759	D	856935	1277	858212	0.49137	SRR10002921
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+Eukaryota	2759	D	964946	1212	966158	0.48844	SRR10002935
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+Eukaryota	2759	D	790362	1035	791397	0.34172	SRR10002956
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+Viruses	10239	D	404	47	451	3e-4	SRR10002881
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+Viruses	10239	D	23	14	37	5.3e-4	SRR10002890
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diff --git a/data/2024-04-12_leung/3/hv_clade_counts.tsv.gz b/data/2024-04-12_leung/3/hv_clade_counts.tsv.gz
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index 0000000..5e842ab
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index 0000000..0dafad2
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index 0000000..cad958e
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diff --git a/data/2024-04-12_leung/3/kraken/SRR10002876.report.gz b/data/2024-04-12_leung/3/kraken/SRR10002876.report.gz
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diff --git a/data/2024-04-12_leung/3/kraken/SRR10002877.report.gz b/data/2024-04-12_leung/3/kraken/SRR10002877.report.gz
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diff --git a/data/2024-04-12_leung/3/kraken/SRR10002878.report.gz b/data/2024-04-12_leung/3/kraken/SRR10002878.report.gz
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@@ -0,0 +1,94 @@
+sample,library,country,region,city,location,instrument,date
+SRR10002875,SRR10002875,Hong Kong,Asia,Hong Kong,"Hong Kong, Yau Tong",SASS 3100 electret filter air sampler,2017-07-05
+SRR10002876,SRR10002876,Hong Kong,Asia,Hong Kong,"Hong Kong, Kowloon Tong",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002877,SRR10002877,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002878,SRR10002878,uncalculated,uncalculated,,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002879,SRR10002879,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002880,SRR10002880,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002881,SRR10002881,Sweden,Europe,Stockholm,"Stockholm, Fridhemsplan",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002882,SRR10002882,Sweden,Europe,Stockholm,"Stockholm, Skanstull",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002883,SRR10002883,Sweden,Europe,Stockholm,"Stockholm, Skanstull",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002884,SRR10002884,Sweden,Europe,Stockholm,"Stockholm, Fridhemsplan",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002885,SRR10002885,Sweden,Europe,Stockholm,"Stockholm, Slussen",SASS 3100 electret filter air sampler,2017-09-27
+SRR10002886,SRR10002886,Sweden,Europe,Stockholm,"Stockholm, Medborgarplatsen",SASS 3100 electret filter air sampler,2017-09-27
+SRR10002887,SRR10002887,Sweden,Europe,Stockholm,"Stockholm, Tekniska hoegskolan",SASS 3100 electret filter air sampler,2017-09-27
+SRR10002888,SRR10002888,Sweden,Europe,Stockholm,"Stockholm, T-centralen",SASS 3100 electret filter air sampler,2017-09-27
+SRR10002889,SRR10002889,Norway,Europe,Oslo,"Oslo, Groenland",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002890,SRR10002890,uncalculated,uncalculated,,"Oslo, Groenland",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002891,SRR10002891,Hong Kong,Asia,Hong Kong,"Hong Kong, Cheung Sha Wan",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002892,SRR10002892,Hong Kong,Asia,Hong Kong,"Hong Kong, Ocean Park",SASS 3100 electret filter air sampler,2017-07-05
+SRR10002893,SRR10002893,Sweden,Europe,Stockholm,"Stockholm, Medborgarplatsen",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002894,SRR10002894,Sweden,Europe,Stockholm,"Stockholm, Slussen",SASS 3100 electret filter air sampler,2017-09-28
+SRR10002895,SRR10002895,Hong Kong,Asia,Hong Kong,"Hong Kong, Sham Shui Po",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002896,SRR10002896,USA,North America,Denver,"Denver, Denver Airport",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002897,SRR10002897,Hong Kong,Asia,Hong Kong,"Hong Kong, Tsing Yi",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002898,SRR10002898,Hong Kong,Asia,Hong Kong,"Hong Kong, Tai Wai",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002899,SRR10002899,Hong Kong,Asia,Hong Kong,"Hong Kong, Lai King",SASS 3100 electret filter air sampler,2017-07-04
+SRR10002900,SRR10002900,Norway,Europe,Oslo,"Oslo, Nationaltheateret",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002901,SRR10002901,USA,North America,New York City,"New York City, 68th St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002902,SRR10002902,USA,North America,New York City,"New York City, 63rd St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002903,SRR10002903,Norway,Europe,Oslo,"Oslo, Ellingsrudaasen",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002904,SRR10002904,Norway,Europe,Oslo,"Oslo, Lindeberg",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002905,SRR10002905,USA,North America,New York City,"New York City, 68th St.",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002906,SRR10002906,USA,North America,New York City,"New York City, 57th St",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002907,SRR10002907,USA,North America,New York City,"New York City, 72nd St.",SASS 3100 electret filter air sampler,2017-06-22
+SRR10002908,SRR10002908,USA,North America,New York City,"New York City, 77th St.",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002909,SRR10002909,USA,North America,Denver,"Denver, Denver Airport",SASS 3100 electret filter air sampler,2017-06-29
+SRR10002910,SRR10002910,USA,North America,Denver,"Denver, Denver Airport",SASS 3100 electret filter air sampler,2017-06-29
+SRR10002911,SRR10002911,Norway,Europe,Oslo,"Oslo, Vestli",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002912,SRR10002912,Norway,Europe,Oslo,"Oslo, Romsaas",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002913,SRR10002913,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002914,SRR10002914,Hong Kong,Asia,Hong Kong,"Hong Kong, Admiralty",SASS 3100 electret filter air sampler,2017-07-05
+SRR10002915,SRR10002915,Sweden,Europe,Stockholm,"Stockholm, Tekniska hoegskolan",SASS 3100 electret filter air sampler,2017-09-26
+SRR10002916,SRR10002916,Sweden,Europe,Stockholm,"Stockholm, T-centralen",SASS 3100 electret filter air sampler,2017-09-26
+SRR10002917,SRR10002917,Sweden,Europe,Stockholm,"Stockholm, Danderyd",SASS 3100 electret filter air sampler,2017-09-27
+SRR10002918,SRR10002918,Sweden,Europe,Stockholm,"Stockholm, Universitetet",SASS 3100 electret filter air sampler,2017-09-27
+SRR10002919,SRR10002919,Sweden,Europe,Stockholm,"Stockholm, Skanstull",SASS 3100 electret filter air sampler,2017-09-26
+SRR10002920,SRR10002920,Sweden,Europe,Stockholm,"Stockholm, Fridhemsplan",SASS 3100 electret filter air sampler,2017-09-26
+SRR10002921,SRR10002921,Sweden,Europe,Stockholm,"Stockholm, Slussen",SASS 3100 electret filter air sampler,2017-09-26
+SRR10002922,SRR10002922,Sweden,Europe,Stockholm,"Stockholm, Medborgarplatsen",SASS 3100 electret filter air sampler,2017-09-26
+SRR10002923,SRR10002923,Hong Kong,Asia,Hong Kong,"Hong Kong, Shek Kip Mei",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002924,SRR10002924,Hong Kong,Asia,Hong Kong,"Hong Kong, Prince Edward",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002925,SRR10002925,Hong Kong,Asia,Hong Kong,"Hong Kong, North Point",SASS 3100 electret filter air sampler,2017-07-05
+SRR10002926,SRR10002926,Sweden,Europe,Stockholm,"Stockholm, Universitetet",SASS 3100 electret filter air sampler,2017-09-27
+SRR10002927,SRR10002927,Sweden,Europe,Stockholm,"Stockholm, Danderyd",SASS 3100 electret filter air sampler,2017-09-27
+SRR10002928,SRR10002928,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002929,SRR10002929,Hong Kong,Asia,Hong Kong,"Hong Kong, Sha Tin Wai",SASS 3100 electret filter air sampler,2017-07-13
+SRR10002930,SRR10002930,USA,North America,Denver,"Denver, Blake & 38th",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002931,SRR10002931,Norway,Europe,Oslo,"Oslo, Jernbanetorget",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002932,SRR10002932,Sweden,Europe,Stockholm,"Stockholm, Skanstull",SASS 3100 electret filter air sampler,2017-09-26
+SRR10002933,SRR10002933,Sweden,Europe,Stockholm,"Stockholm, Fridhemsplan",SASS 3100 electret filter air sampler,2017-09-26
+SRR10002934,SRR10002934,uncalculated,uncalculated,,"Stockholm, Fridhemsplan",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002935,SRR10002935,United Kingdom,Europe,London,"London, Euston",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002936,SRR10002936,United Kingdom,Europe,London,"London, Euston",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002937,SRR10002937,Sweden,Europe,Stockholm,"Stockholm, Danderyd",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002938,SRR10002938,Sweden,Europe,Stockholm,"Stockholm, Universitetet",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002939,SRR10002939,United Kingdom,Europe,London,"London, Brixton",SASS 3100 electret filter air sampler,2017-07-23
+SRR10002940,SRR10002940,United Kingdom,Europe,London,"London, Brixton",SASS 3100 electret filter air sampler,2017-08-21
+SRR10002941,SRR10002941,United Kingdom,Europe,London,"London, Oxford Circus",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002942,SRR10002942,United Kingdom,Europe,London,"London, Oxford Circus",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002943,SRR10002943,United Kingdom,Europe,London,"London, Pimlico",SASS 3100 electret filter air sampler,2017-08-18
+SRR10002944,SRR10002944,United Kingdom,Europe,London,"London, Pimlico",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002945,SRR10002945,Hong Kong,Asia,Hong Kong,"Hong Kong, Shek Kip Mei",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002946,SRR10002946,Hong Kong,Asia,Hong Kong,"Hong Kong, Prince Edward",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002947,SRR10002947,Norway,Europe,Oslo,"Oslo, Montebello",SASS 3100 electret filter air sampler,2017-06-21
+SRR10002948,SRR10002948,USA,North America,Denver,"Denver, Union Station",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002949,SRR10002949,Hong Kong,Asia,Hong Kong,"Hong Kong, Hung Hom",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002950,SRR10002950,Hong Kong,Asia,Hong Kong,"Hong Kong, Mong Kok East",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002951,SRR10002951,Hong Kong,Asia,Hong Kong,"Hong Kong, Tsim Sha Tsui",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002952,SRR10002952,Hong Kong,Asia,Hong Kong,"Hong Kong, East Tsim Sha Tsui",SASS 3100 electret filter air sampler,2017-07-07
+SRR10002953,SRR10002953,Hong Kong,Asia,Hong Kong,"Hong Kong, Che Kung Temple",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002954,SRR10002954,Hong Kong,Asia,Hong Kong,"Hong Kong, Sha Tin Wai",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002955,SRR10002955,Hong Kong,Asia,Hong Kong,"Hong Kong, Kowloon Tong",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002956,SRR10002956,Hong Kong,Asia,Hong Kong,"Hong Kong, Tai Wai",SASS 3100 electret filter air sampler,2017-07-11
+SRR10002957,SRR10002957,Norway,Europe,Oslo,"Oslo, Romsaas",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002958,SRR10002958,Norway,Europe,Oslo,"Oslo, Vestli",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002959,SRR10002959,USA,North America,Denver,"Denver, Boulder Central",SASS 3100 electret filter air sampler,2017-06-27
+SRR10002960,SRR10002960,Norway,Europe,Oslo,"Oslo, Lindeberg",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002961,SRR10002961,Norway,Europe,Oslo,"Oslo, Ellingsrudaasen",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002962,SRR10002962,Norway,Europe,Oslo,"Oslo, Toeyen",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002963,SRR10002963,Norway,Europe,Oslo,"Oslo, Helsfyr",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002964,SRR10002964,Norway,Europe,Oslo,"Oslo, Forskningsparken",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002965,SRR10002965,Norway,Europe,Oslo,"Oslo, Majorstua",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002966,SRR10002966,Norway,Europe,Oslo,"Oslo, Loeren",SASS 3100 electret filter air sampler,2017-06-26
+SRR10002967,SRR10002967,Norway,Europe,Oslo,"Oslo, Nydalen",SASS 3100 electret filter air sampler,2017-06-26
diff --git a/data/2024-04-12_leung/3/taxonomic_composition.tsv b/data/2024-04-12_leung/3/taxonomic_composition.tsv
new file mode 100644
index 0000000..62bf5c7
--- /dev/null
+++ b/data/2024-04-12_leung/3/taxonomic_composition.tsv
@@ -0,0 +1,745 @@
+sample	classification	n_reads	p_reads
+SRR10002875	Filtered	368155	0.06675752505662481
+SRR10002875	Duplicate	515777	0.0935258138586486
+SRR10002875	Ribosomal	11500	0.002085294341109547
+SRR10002875	Unassigned	1957216	0.35490186514165767
+SRR10002875	Bacterial	1275312	0.23125225189122597
+SRR10002875	Archaeal	3906	7.082747562064253e-4
+SRR10002875	Viral	856	1.5521843095563236e-4
+SRR10002875	Human	1382087	0.2506137565235714
+SRR10002876	Filtered	341697	0.07645533230840074
+SRR10002876	Duplicate	451638	0.10105483329704824
+SRR10002876	Ribosomal	10493	0.0023478280520813732
+SRR10002876	Unassigned	1855389	0.415146701774822
+SRR10002876	Bacterial	945275	0.21150702010208902
+SRR10002876	Archaeal	1128	2.5239207497834644e-4
+SRR10002876	Viral	469	1.049396127347912e-4
+SRR10002876	Human	863148	0.19313095277784553
+SRR10002877	Filtered	230163	0.06481728415852542
+SRR10002877	Duplicate	236430	0.06658216348240233
+SRR10002877	Ribosomal	15734	0.004430925687231392
+SRR10002877	Unassigned	1787706	0.5034442885863534
+SRR10002877	Bacterial	481607	0.13562761074427668
+SRR10002877	Archaeal	383	1.0785843003747447e-4
+SRR10002877	Viral	502	1.4137057931804748e-4
+SRR10002877	Human	798426	0.22484849833185533
+SRR10002878	Filtered	308735	0.7826438550283032
+SRR10002878	Duplicate	7557	0.019157010421393388
+SRR10002878	Ribosomal	510	0.0012928510407450879
+SRR10002878	Unassigned	28416	0.07203461798786748
+SRR10002878	Bacterial	35798	0.09074800305214245
+SRR10002878	Archaeal	15	3.8025030610149644e-5
+SRR10002878	Viral	20	5.070004081353286e-5
+SRR10002878	Human	13426	0.034034937398124604
+SRR10002879	Filtered	375141	0.08798707756276868
+SRR10002879	Duplicate	234914	0.055097673510973846
+SRR10002879	Ribosomal	32051	0.007517370329994052
+SRR10002879	Unassigned	3121716	0.7321798145788809
+SRR10002879	Bacterial	441071	0.10345056468817841
+SRR10002879	Archaeal	776	1.8200615818774404e-4
+SRR10002879	Viral	298	6.989411744838624e-5
+SRR10002879	Human	57625	0.013515599053567978
+SRR10002880	Filtered	299773	0.06738081478789203
+SRR10002880	Duplicate	289019	0.06496360816078088
+SRR10002880	Ribosomal	19468	0.004375876754379754
+SRR10002880	Unassigned	1966614	0.44204132357909315
+SRR10002880	Bacterial	564922	0.12697909635492702
+SRR10002880	Archaeal	473	1.0631753158113949e-4
+SRR10002880	Viral	568	1.2767094701498358e-4
+SRR10002880	Human	1308100	0.294025291884331
+SRR10002881	Filtered	426429	0.09457795130675706
+SRR10002881	Duplicate	227691	0.05049972753022618
+SRR10002881	Ribosomal	14223	0.0031545279552657197
+SRR10002881	Unassigned	2325536	0.51578206587758
+SRR10002881	Bacterial	710111	0.15749595731151622
+SRR10002881	Archaeal	1377	3.0540568054565815e-4
+SRR10002881	Viral	451	1.0002756857377766e-4
+SRR10002881	Human	802939	0.17808433676953536
+SRR10002882	Filtered	295345	0.07610052602625364
+SRR10002882	Duplicate	217922	0.056151209035850434
+SRR10002882	Ribosomal	10364	0.0026704560826697346
+SRR10002882	Unassigned	2008478	0.5175175889625958
+SRR10002882	Bacterial	659490	0.16992851041681428
+SRR10002882	Archaeal	644	1.6593725561938529e-4
+SRR10002882	Viral	652	1.6799858798732795e-4
+SRR10002882	Human	688090	0.17729777363220936
+SRR10002883	Filtered	478341	0.0759836360875292
+SRR10002883	Duplicate	368515	0.058537966958290895
+SRR10002883	Ribosomal	15717	0.002496618120520082
+SRR10002883	Unassigned	3031603	0.4815648650520482
+SRR10002883	Bacterial	972555	0.15448867062431815
+SRR10002883	Archaeal	926	1.4709348982640426e-4
+SRR10002883	Viral	937	1.4884082069907214e-4
+SRR10002883	Human	1426722	0.22663230884676797
+SRR10002884	Filtered	343008	0.08889047834142011
+SRR10002884	Duplicate	250089	0.06481053164336521
+SRR10002884	Ribosomal	14589	0.00378073744205085
+SRR10002884	Unassigned	1782776	0.4620061672485877
+SRR10002884	Bacterial	540907	0.14017597831019254
+SRR10002884	Archaeal	888	2.3012508386737642e-4
+SRR10002884	Viral	496	1.2853833513312918e-4
+SRR10002884	Human	926018	0.2399774435953831
+SRR10002885	Filtered	417525	0.08088661924831286
+SRR10002885	Duplicate	278810	0.05401352808244323
+SRR10002885	Ribosomal	13577	0.002630255983556299
+SRR10002885	Unassigned	2734783	0.5298062421358213
+SRR10002885	Bacterial	850177	0.16470377412771184
+SRR10002885	Archaeal	1087	2.105832108805846e-4
+SRR10002885	Viral	551	1.0674457147672687e-4
+SRR10002885	Human	865345	0.16764225263979712
+SRR10002886	Filtered	278980	0.0694535382268311
+SRR10002886	Duplicate	236314	0.05883161313547697
+SRR10002886	Ribosomal	9121	0.002270720919660644
+SRR10002886	Unassigned	1956765	0.4871469378752067
+SRR10002886	Bacterial	640402	0.15943144593712485
+SRR10002886	Archaeal	637	1.585845001451409e-4
+SRR10002886	Viral	535	1.3319106370117802e-4
+SRR10002886	Human	894032	0.22257396834185342
+SRR10002887	Filtered	342376	0.08195532690714485
+SRR10002887	Duplicate	236216	0.056543564679469735
+SRR10002887	Ribosomal	10041	0.002403537156443914
+SRR10002887	Unassigned	2370742	0.5674899397811132
+SRR10002887	Bacterial	516540	0.12364536229355037
+SRR10002887	Archaeal	2890	6.91785915956868e-4
+SRR10002887	Viral	437	1.0460569040593471e-4
+SRR10002887	Human	698351	0.16716587757591514
+SRR10002888	Filtered	391314	0.06712359854697103
+SRR10002888	Duplicate	299387	0.05135500594965173
+SRR10002888	Ribosomal	14493	0.0024860401461262597
+SRR10002888	Unassigned	2885402	0.49494412542006494
+SRR10002888	Bacterial	755728	0.12963293642114854
+SRR10002888	Archaeal	868	1.488913852782442e-4
+SRR10002888	Viral	757	1.298511274834457e-4
+SRR10002888	Human	1481804	0.25417955100327577
+SRR10002889	Filtered	625176	0.07938447496493146
+SRR10002889	Duplicate	745528	0.09466670001992307
+SRR10002889	Ribosomal	24499	0.0031108683829287366
+SRR10002889	Unassigned	3978860	0.5052332656067526
+SRR10002889	Bacterial	1693075	0.2149856519624095
+SRR10002889	Archaeal	10479	0.0013306171592599792
+SRR10002889	Viral	519	6.59023099204055e-5
+SRR10002889	Human	797157	0.10122251959387416
+SRR10002890	Filtered	820411	0.8870276646423015
+SRR10002890	Duplicate	10661	0.011526663992500803
+SRR10002890	Ribosomal	778	8.41172928071065e-4
+SRR10002890	Unassigned	22581	0.02441455769765131
+SRR10002890	Bacterial	54159	0.058556664024936776
+SRR10002890	Archaeal	8	8.649593090705038e-6
+SRR10002890	Viral	37	4.000436804451081e-5
+SRR10002890	Human	16264	0.017584622753403344
+SRR10002891	Filtered	241025	0.061437352380845564
+SRR10002891	Duplicate	329525	0.08399603171164043
+SRR10002891	Ribosomal	8476	0.002160535209127879
+SRR10002891	Unassigned	777202	0.1981090473813834
+SRR10002891	Bacterial	522386	0.13315636452990517
+SRR10002891	Archaeal	838	2.1360647773114235e-4
+SRR10002891	Viral	386	9.839152792866461e-5
+SRR10002891	Human	2043264	0.5208286707814378
+SRR10002892	Filtered	444299	0.07934420784193269
+SRR10002892	Duplicate	489961	0.08749866062818323
+SRR10002892	Ribosomal	11872	0.0021201362944760736
+SRR10002892	Unassigned	1501770	0.26819045510068507
+SRR10002892	Bacterial	1295961	0.23143648520262017
+SRR10002892	Archaeal	1742	3.110914273060411e-4
+SRR10002892	Viral	1516	0.00027073168989435033
+SRR10002892	Human	1852519	0.3308282318149024
+SRR10002893	Filtered	405115	0.10871327004170721
+SRR10002893	Duplicate	334907	0.08987283889724655
+SRR10002893	Ribosomal	8603	0.002308629061300636
+SRR10002893	Unassigned	1751713	0.4700750364824039
+SRR10002893	Bacterial	610075	0.16371461985039934
+SRR10002893	Archaeal	602	1.6154768044902742e-4
+SRR10002893	Viral	474	1.271986719814601e-4
+SRR10002893	Human	614965	0.16502685931451186
+SRR10002894	Filtered	512823	0.11766005569788235
+SRR10002894	Duplicate	480245	0.11018548982520189
+SRR10002894	Ribosomal	10536	0.002417337652236519
+SRR10002894	Unassigned	2005684	0.46017610589297175
+SRR10002894	Bacterial	483662	0.11096947262300867
+SRR10002894	Archaeal	593	1.360555455368504e-4
+SRR10002894	Viral	518	1.188478458483786e-4
+SRR10002894	Human	864453	0.19833663491731357
+SRR10002895	Filtered	303666	0.05398355992552741
+SRR10002895	Duplicate	533309	0.09480784269665718
+SRR10002895	Ribosomal	15457	0.0027478344159994113
+SRR10002895	Unassigned	1695850	0.3014760299134762
+SRR10002895	Bacterial	1262439	0.224427335983689
+SRR10002895	Archaeal	1235	2.19549427687085e-4
+SRR10002895	Viral	995	1.7688395186125471e-4
+SRR10002895	Human	1812206	0.3221609636851025
+SRR10002896	Filtered	320427	0.08970181698060124
+SRR10002896	Duplicate	230731	0.0645919037214439
+SRR10002896	Ribosomal	10251	0.002869712370893037
+SRR10002896	Unassigned	1217516	0.34083706242905154
+SRR10002896	Bacterial	777374	0.2176216744327972
+SRR10002896	Archaeal	460	1.2877452839828282e-4
+SRR10002896	Viral	454	1.270948606365661e-4
+SRR10002896	Human	1014922	0.28412196067617823
+SRR10002897	Filtered	246400	0.06358325716588985
+SRR10002897	Duplicate	378640	0.09770764810589502
+SRR10002897	Ribosomal	11063	0.0028547953491324654
+SRR10002897	Unassigned	1203432	0.31054434390284563
+SRR10002897	Bacterial	1007135	0.2598901124422422
+SRR10002897	Archaeal	1087	2.804991905004962e-4
+SRR10002897	Viral	932	2.4050160583851193e-4
+SRR10002897	Human	1026545	0.26489884223765586
+SRR10002898	Filtered	381478	0.07668055170567283
+SRR10002898	Duplicate	548167	0.11018655856128938
+SRR10002898	Ribosomal	10189	0.0020480817801527226
+SRR10002898	Unassigned	2353351	0.47304498041065757
+SRR10002898	Bacterial	1218141	0.24485743328658532
+SRR10002898	Archaeal	1916	3.8513344693027937e-4
+SRR10002898	Viral	337	6.774006869285186e-5
+SRR10002898	Human	461320	0.09272952074001904
+SRR10002899	Filtered	307634	0.06638172002870757
+SRR10002899	Duplicate	482336	0.10407917626714437
+SRR10002899	Ribosomal	12601	0.002719062438097688
+SRR10002899	Unassigned	1502500	0.32421167472754353
+SRR10002899	Bacterial	1043899	0.22525407190443125
+SRR10002899	Archaeal	1228	2.6497965827981595e-4
+SRR10002899	Viral	905	1.9528224001891972e-4
+SRR10002899	Human	1283215	0.2768940327357769
+SRR10002900	Filtered	354543	0.06876146814790708
+SRR10002900	Duplicate	474744	0.09207372429976053
+SRR10002900	Ribosomal	15789	0.00306218095008872
+SRR10002900	Unassigned	2769642	0.537155296153374
+SRR10002900	Bacterial	959936	0.1861737749385246
+SRR10002900	Archaeal	5574	0.001081043550306829
+SRR10002900	Viral	338	6.555305346316975e-5
+SRR10002900	Human	575563	0.11162695890657506
+SRR10002901	Filtered	273454	0.0982579015696241
+SRR10002901	Duplicate	161194	0.05792046993503108
+SRR10002901	Ribosomal	9016	0.003239642647581425
+SRR10002901	Unassigned	1397211	0.502047952891514
+SRR10002901	Bacterial	508376	0.18267042708594217
+SRR10002901	Archaeal	686	2.464945492724997e-4
+SRR10002901	Viral	663	2.3823015476336345e-4
+SRR10002901	Human	432423	0.15537888116627135
+SRR10002902	Filtered	404286	0.09510987064327364
+SRR10002902	Duplicate	231120	0.05437188847269855
+SRR10002902	Ribosomal	11537	0.0027141245989508616
+SRR10002902	Unassigned	1992665	0.4687822738986234
+SRR10002902	Bacterial	821680	0.19330344981069117
+SRR10002902	Archaeal	2823	6.641218464798719e-4
+SRR10002902	Viral	303	7.128194101431143e-5
+SRR10002902	Human	786312	0.18498298878826816
+SRR10002903	Filtered	356592	0.08781410376382746
+SRR10002903	Duplicate	394870	0.09724041804982318
+SRR10002903	Ribosomal	13473	0.0033178518306917915
+SRR10002903	Unassigned	2226208	0.5482244702962007
+SRR10002903	Bacterial	694034	0.170912341532127
+SRR10002903	Archaeal	1191	2.9329485121011833e-4
+SRR10002903	Viral	687	1.691801534688088e-4
+SRR10002903	Human	373705	0.09202833952265094
+SRR10002904	Filtered	238480	0.06718425891519735
+SRR10002904	Duplicate	293916	0.08280161289550127
+SRR10002904	Ribosomal	8062	0.0022712155961687394
+SRR10002904	Unassigned	1017186	0.2865602465150701
+SRR10002904	Bacterial	244550	0.06889429100013213
+SRR10002904	Archaeal	390	1.0987026575363537e-4
+SRR10002904	Viral	121	3.408795424664072e-5
+SRR10002904	Human	1746936	0.49214441685793014
+SRR10002905	Filtered	358440	0.08407372424034239
+SRR10002905	Duplicate	205246	0.048141378209556177
+SRR10002905	Ribosomal	15194	0.0035638214655388974
+SRR10002905	Unassigned	2385302	0.5594833795835766
+SRR10002905	Bacterial	897866	0.21059853389348082
+SRR10002905	Archaeal	1259	2.95304148026423e-4
+SRR10002905	Viral	383	8.983438339485308e-5
+SRR10002905	Human	399711	0.09375402407608385
+SRR10002906	Filtered	441970	0.07880384053941146
+SRR10002906	Duplicate	305462	0.05446428205274047
+SRR10002906	Ribosomal	20011	0.0035679879924749706
+SRR10002906	Unassigned	2924224	0.52139303979347
+SRR10002906	Bacterial	1236976	0.22055447079005142
+SRR10002906	Archaeal	1926	3.4340836907234985e-4
+SRR10002906	Viral	581	1.0359307499015331e-4
+SRR10002906	Human	677333	0.12076937738778917
+SRR10002907	Filtered	449083	0.0914626720005279
+SRR10002907	Duplicate	254037	0.05173854901432053
+SRR10002907	Ribosomal	13120	0.0026720901406798434
+SRR10002907	Unassigned	2525025	0.5142602444718081
+SRR10002907	Bacterial	1218228	0.24811090151677775
+SRR10002907	Archaeal	1128	2.2973457916820604e-4
+SRR10002907	Viral	766	1.5600770181103352e-4
+SRR10002907	Human	448627	0.0913698005749067
+SRR10002908	Filtered	513627	0.08069457154864633
+SRR10002908	Duplicate	341063	0.05358350058718868
+SRR10002908	Ribosomal	9718	0.001526769126836683
+SRR10002908	Unassigned	3694662	0.5804585177708039
+SRR10002908	Bacterial	1689393	0.2654160398738428
+SRR10002908	Archaeal	2048	3.2175583162806406e-4
+SRR10002908	Viral	426	6.692772669607192e-5
+SRR10002908	Human	114138	0.01793191753435741
+SRR10002909	Filtered	355749	0.08770484346582903
+SRR10002909	Duplicate	234389	0.057785265890029774
+SRR10002909	Ribosomal	15425	0.0038028138110308475
+SRR10002909	Unassigned	2049394	0.5052488692021881
+SRR10002909	Bacterial	774472	0.19093502871031975
+SRR10002909	Archaeal	1162	2.864745315019672e-4
+SRR10002909	Viral	464	1.1439258400767022e-4
+SRR10002909	Human	625152	0.15412231180509278
+SRR10002910	Filtered	411090	0.10255531348486742
+SRR10002910	Duplicate	318191	0.07937964375942846
+SRR10002910	Ribosomal	15573	0.003885022493614149
+SRR10002910	Unassigned	1737805	0.433533135202924
+SRR10002910	Bacterial	837194	0.2088561947934761
+SRR10002910	Archaeal	621	1.5492191411638003e-4
+SRR10002910	Viral	686	1.7113757340392384e-4
+SRR10002910	Human	687311	0.17146463077816954
+SRR10002911	Filtered	537777	0.07454250663987567
+SRR10002911	Duplicate	675995	0.09370122146544527
+SRR10002911	Ribosomal	18760	0.0026003667404222715
+SRR10002911	Unassigned	4004665	0.5550958247618952
+SRR10002911	Bacterial	1406337	0.19493560557703815
+SRR10002911	Archaeal	2946	4.0835183461002193e-4
+SRR10002911	Viral	407	5.641520593560045e-5
+SRR10002911	Human	567480	0.07865970777477775
+SRR10002912	Filtered	653552	0.09457796620152288
+SRR10002912	Duplicate	715881	0.1035978300461362
+SRR10002912	Ribosomal	22971	0.0033242197432112246
+SRR10002912	Unassigned	3584987	0.518796942429828
+SRR10002912	Bacterial	1161927	0.1681468231060985
+SRR10002912	Archaeal	5820	8.422340736358594e-4
+SRR10002912	Viral	569	8.234212850494913e-5
+SRR10002912	Human	764486	0.11063164227106247
+SRR10002913	Filtered	429873	0.10074774568912481
+SRR10002913	Duplicate	241266	0.056544620414476804
+SRR10002913	Ribosomal	11886	0.0027856778752350987
+SRR10002913	Unassigned	1334925	0.3128614367826194
+SRR10002913	Bacterial	499284	0.11701534513367667
+SRR10002913	Archaeal	432	1.0124624281520803e-4
+SRR10002913	Viral	425	9.960567869551716e-5
+SRR10002913	Human	1748734	0.4098443221833565
+SRR10002914	Filtered	452490	0.07114177446128371
+SRR10002914	Duplicate	589373	0.09266291197500534
+SRR10002914	Ribosomal	13759	0.0021632294079710107
+SRR10002914	Unassigned	1753511	0.27569202430413947
+SRR10002914	Bacterial	1746682	0.2746183493548674
+SRR10002914	Archaeal	1249	1.963713591507953e-4
+SRR10002914	Viral	1215	1.9102578171994897e-4
+SRR10002914	Human	1802119	0.28333431335586234
+SRR10002915	Filtered	486379	0.08997775803262834
+SRR10002915	Duplicate	293634	0.054320867064887236
+SRR10002915	Ribosomal	22660	0.004191990190816951
+SRR10002915	Unassigned	3302461	0.6109392814455226
+SRR10002915	Bacterial	664801	0.1229849634088835
+SRR10002915	Archaeal	5184	9.59014878605255e-4
+SRR10002915	Viral	449	8.306282416932088e-5
+SRR10002915	Human	629979	0.11654306215448686
+SRR10002916	Filtered	416420	0.07181152071181734
+SRR10002916	Duplicate	324743	0.05600184590201647
+SRR10002916	Ribosomal	13334	0.0022994448325521647
+SRR10002916	Unassigned	2684930	0.46301548029580647
+SRR10002916	Bacterial	824380	0.14216411662362033
+SRR10002916	Archaeal	1189	2.05042740805799e-4
+SRR10002916	Viral	729	1.2571586042676826e-4
+SRR10002916	Human	1533066	0.2643768330329546
+SRR10002917	Filtered	408928	0.08059258792100536
+SRR10002917	Duplicate	252449	0.04975330187238311
+SRR10002917	Ribosomal	16331	0.003218555719681554
+SRR10002917	Unassigned	3197660	0.6302031034594892
+SRR10002917	Bacterial	619121	0.12201796801940869
+SRR10002917	Archaeal	1291	2.544336191359308e-4
+SRR10002917	Viral	436	8.592800770198748e-5
+SRR10002917	Human	577799	0.11387412138119418
+SRR10002918	Filtered	460344	0.10031715758465126
+SRR10002918	Duplicate	238707	0.05201850732399977
+SRR10002918	Ribosomal	14105	0.003073730748595629
+SRR10002918	Unassigned	2611927	0.5691854188576487
+SRR10002918	Bacterial	698856	0.15229317093516814
+SRR10002918	Archaeal	2532	5.517679018393571e-4
+SRR10002918	Viral	424	9.23971525986917e-5
+SRR10002918	Human	561991	0.12246784949549847
+SRR10002919	Filtered	410129	0.08378559921160078
+SRR10002919	Duplicate	280921	0.05738958794945518
+SRR10002919	Ribosomal	14580	0.0029785604931744387
+SRR10002919	Unassigned	2452789	0.501082332887026
+SRR10002919	Bacterial	661329	0.1351034590116981
+SRR10002919	Archaeal	551	1.1256425457744277e-4
+SRR10002919	Viral	764	1.560782041690858e-4
+SRR10002919	Human	1073919	0.21939181798829904
+SRR10002920	Filtered	344098	0.07656274231937285
+SRR10002920	Duplicate	278880	0.06205155966621921
+SRR10002920	Ribosomal	12644	0.0028133244421244827
+SRR10002920	Unassigned	2094472	0.46602572531994224
+SRR10002920	Bacterial	694012	0.15441956048146918
+SRR10002920	Archaeal	842	1.8734729359924188e-4
+SRR10002920	Viral	610	1.3572666163365506e-4
+SRR10002920	Human	1068769	0.23780401381563915
+SRR10002921	Filtered	374987	0.06128053633277367
+SRR10002921	Duplicate	370618	0.060566552479365716
+SRR10002921	Ribosomal	14899	0.0024348009686255656
+SRR10002921	Unassigned	3612113	0.590293055318142
+SRR10002921	Bacterial	885433	0.1446978405297698
+SRR10002921	Archaeal	1006	1.6440095136836827e-4
+SRR10002921	Viral	1918	3.1344038242995067e-4
+SRR10002921	Human	858212	0.14024937303752494
+SRR10002922	Filtered	544174	0.08599686938739712
+SRR10002922	Duplicate	385948	0.06099210867540004
+SRR10002922	Ribosomal	14716	0.0023255979335744374
+SRR10002922	Unassigned	3329493	0.5261662164073494
+SRR10002922	Bacterial	977141	0.15441948154463572
+SRR10002922	Archaeal	1007	1.5913815704739456e-4
+SRR10002922	Viral	655	1.0351091645088723e-4
+SRR10002922	Human	1074701	0.16983707697814499
+SRR10002923	Filtered	334124	0.06962663134669612
+SRR10002923	Duplicate	425576	0.08868391154781324
+SRR10002923	Ribosomal	8891	0.0018527563997302657
+SRR10002923	Unassigned	1086384	0.22638678535199247
+SRR10002923	Bacterial	1111323	0.23158371391490698
+SRR10002923	Archaeal	1754	3.6550834834404297e-4
+SRR10002923	Viral	554	1.1544562427742292e-4
+SRR10002923	Human	1830190	0.3813852474662395
+SRR10002924	Filtered	315088	0.060901127477234654
+SRR10002924	Duplicate	475817	0.09196729730372265
+SRR10002924	Ribosomal	11396	0.0022026521122053716
+SRR10002924	Unassigned	1682176	0.3251358827221115
+SRR10002924	Bacterial	1509173	0.291697358383057
+SRR10002924	Archaeal	1627	3.144713045417813e-4
+SRR10002924	Viral	851	1.644837616257258e-4
+SRR10002924	Human	1177635	0.2276167269355013
+SRR10002925	Filtered	548664	0.08104980790954443
+SRR10002925	Duplicate	589008	0.08700950902042953
+SRR10002925	Ribosomal	16526	0.0024412557148147706
+SRR10002925	Unassigned	1957912	0.2892269066382922
+SRR10002925	Bacterial	1700766	0.2512407549959251
+SRR10002925	Archaeal	1666	2.461050478567958e-4
+SRR10002925	Viral	2321	3.428630348593176e-4
+SRR10002925	Human	1952604	0.28844279763827785
+SRR10002926	Filtered	350157	0.11391694688515033
+SRR10002926	Duplicate	157511	0.05124322010077455
+SRR10002926	Ribosomal	10647	0.003463799762638461
+SRR10002926	Unassigned	1575694	0.5126221943449655
+SRR10002926	Bacterial	383093	0.12463205057466478
+SRR10002926	Archaeal	794	2.583128591654868e-4
+SRR10002926	Viral	319	1.037806071458316e-4
+SRR10002926	Human	595577	0.19375969486549513
+SRR10002927	Filtered	428564	0.07151349517610468
+SRR10002927	Duplicate	354935	0.0592271922287703
+SRR10002927	Ribosomal	14782	0.0024666385550190387
+SRR10002927	Unassigned	2789984	0.4655582534356811
+SRR10002927	Bacterial	762255	0.12719574967907166
+SRR10002927	Archaeal	831	1.3866707070902593e-4
+SRR10002927	Viral	829	1.3833333528012333e-4
+SRR10002927	Human	1640591	0.27376167051936406
+SRR10002928	Filtered	423704	0.0854564809365474
+SRR10002928	Duplicate	371813	0.07499063154102982
+SRR10002928	Ribosomal	18608	0.003753030883039277
+SRR10002928	Unassigned	2669142	0.538336863564984
+SRR10002928	Bacterial	882465	0.1779835768594828
+SRR10002928	Archaeal	968	1.9523505453471736e-4
+SRR10002928	Viral	649	1.308962297448673e-4
+SRR10002928	Human	590777	0.1191532849306371
+SRR10002929	Filtered	487921	0.08144298268455498
+SRR10002929	Duplicate	533186	0.088998543136383
+SRR10002929	Ribosomal	12079	0.0020162071069839984
+SRR10002929	Unassigned	1969832	0.3288011654908936
+SRR10002929	Bacterial	1222132	0.20399629307662623
+SRR10002929	Archaeal	1613	2.6923934626750474e-4
+SRR10002929	Viral	633	1.0565933427608834e-4
+SRR10002929	Human	1763556	0.2943699098240146
+SRR10002930	Filtered	513170	0.11614808524391732
+SRR10002930	Duplicate	293199	0.06636105470980633
+SRR10002930	Ribosomal	20457	0.004630125260312989
+SRR10002930	Unassigned	2717560	0.6150776361351208
+SRR10002930	Bacterial	570990	0.12923474714699681
+SRR10002930	Archaeal	790	1.788042702081078e-4
+SRR10002930	Viral	352	7.966975077627082e-5
+SRR10002930	Human	301721	0.06828987748286139
+SRR10002931	Filtered	390406	0.1041114871820035
+SRR10002931	Duplicate	206432	0.055050236220640425
+SRR10002931	Ribosomal	11782	0.00314196385808201
+SRR10002931	Unassigned	1943469	0.5182744319557618
+SRR10002931	Bacterial	664933	0.17732095179477553
+SRR10002931	Archaeal	4122	0.0010992340029718252
+SRR10002931	Viral	296	7.89357750799758e-5
+SRR10002931	Human	528444	0.14092275921068492
+SRR10002932	Filtered	486220	0.06534619560206745
+SRR10002932	Duplicate	464326	0.06240372181137257
+SRR10002932	Ribosomal	22105	0.002970831421545187
+SRR10002932	Unassigned	3716216	0.4994458838294037
+SRR10002932	Bacterial	921528	0.12385000399157174
+SRR10002932	Archaeal	774	1.040227785693723e-4
+SRR10002932	Viral	1454	1.9541229979311026e-4
+SRR10002932	Human	1828055	0.24568392826567687
+SRR10002933	Filtered	365978	0.06995283824899431
+SRR10002933	Duplicate	297614	0.05688578002676717
+SRR10002933	Ribosomal	14429	0.002757951306075062
+SRR10002933	Unassigned	2289938	0.43769751874218
+SRR10002933	Bacterial	634252	0.12123058644263082
+SRR10002933	Archaeal	1332	2.545977642034779e-4
+SRR10002933	Viral	998	1.907571836899932e-4
+SRR10002933	Human	1627241	0.31102997028545915
+SRR10002934	Filtered	199587	0.1505318358613418
+SRR10002934	Duplicate	43468	0.032784288762398375
+SRR10002934	Ribosomal	12597	0.009500866972023842
+SRR10002934	Unassigned	132884	0.10022332354611545
+SRR10002934	Bacterial	924236	0.6970741674013994
+SRR10002934	Archaeal	18	1.3575899459905467e-5
+SRR10002934	Viral	584	4.4046251581026627e-4
+SRR10002934	Human	12505	0.009431479041450992
+SRR10002935	Filtered	338403	0.06333942951220649
+SRR10002935	Duplicate	332895	0.06230848836288679
+SRR10002935	Ribosomal	13827	0.002588021654256254
+SRR10002935	Unassigned	2679529	0.5015317187537142
+SRR10002935	Bacterial	1009275	0.1889076122875158
+SRR10002935	Archaeal	1973	3.6928955838920875e-4
+SRR10002935	Viral	631	1.1810527691008146e-4
+SRR10002935	Human	966158	0.1808373345941212
+SRR10002936	Filtered	298429	0.06703020580810605
+SRR10002936	Duplicate	233791	0.052511849874117195
+SRR10002936	Ribosomal	11377	0.0025553905668645556
+SRR10002936	Unassigned	2226013	0.49998528803004927
+SRR10002936	Bacterial	907739	0.20388746398655752
+SRR10002936	Archaeal	1404	3.1535276047093576e-4
+SRR10002936	Viral	551	1.237602357688644e-4
+SRR10002936	Human	772853	0.17359068873806563
+SRR10002937	Filtered	478281	0.15309802952479787
+SRR10002937	Duplicate	156974	0.050247469764898925
+SRR10002937	Ribosomal	11145	0.0035675210578172086
+SRR10002937	Unassigned	1601987	0.5127969813234111
+SRR10002937	Bacterial	417150	0.1335299604547733
+SRR10002937	Archaeal	1832	5.864242779651077e-4
+SRR10002937	Viral	366	1.1715681535765799e-4
+SRR10002937	Human	456283	0.14605645678097887
+SRR10002938	Filtered	458932	0.09844844569988982
+SRR10002938	Duplicate	255435	0.05479499953664455
+SRR10002938	Ribosomal	15294	0.0032808139953939036
+SRR10002938	Unassigned	2562450	0.5496875782984901
+SRR10002938	Bacterial	701418	0.15046567222578797
+SRR10002938	Archaeal	20976	0.004499696244761509
+SRR10002938	Viral	415	8.902431071586701e-5
+SRR10002938	Human	646728	0.13873376968831624
+SRR10002939	Filtered	507485	0.0658842972631568
+SRR10002939	Duplicate	573818	0.07449598645664425
+SRR10002939	Ribosomal	29620	0.0038454198349403518
+SRR10002939	Unassigned	4350447	0.5647972715954338
+SRR10002939	Bacterial	1326487	0.17221132412527085
+SRR10002939	Archaeal	1252	1.6254104096371778e-4
+SRR10002939	Viral	679	8.815125144917282e-5
+SRR10002939	Human	912882	0.11851500843214106
+SRR10002940	Filtered	361677	0.06801271582059072
+SRR10002940	Duplicate	341441	0.06420737205434217
+SRR10002940	Ribosomal	19310	0.003631211115154148
+SRR10002940	Unassigned	3127387	0.5880995564882747
+SRR10002940	Bacterial	905814	0.1703367097391113
+SRR10002940	Archaeal	887	1.6679877054074206e-4
+SRR10002940	Viral	484	9.10153381530092e-5
+SRR10002940	Human	560785	0.10545462067383318
+SRR10002941	Filtered	338303	0.06553484619400506
+SRR10002941	Duplicate	301823	0.05846807117528721
+SRR10002941	Ribosomal	13670	0.002648103467814501
+SRR10002941	Unassigned	2307161	0.4469349703662306
+SRR10002941	Bacterial	968121	0.1875409347011004
+SRR10002941	Archaeal	1033	2.0010906234472418e-4
+SRR10002941	Viral	971	1.8809864427563134e-4
+SRR10002941	Human	1231103	0.2384848663889419
+SRR10002942	Filtered	426379	0.07101843688783348
+SRR10002942	Duplicate	310259	0.051677285256502614
+SRR10002942	Ribosomal	16736	0.0027875776240264675
+SRR10002942	Unassigned	2942834	0.4901636119517391
+SRR10002942	Bacterial	1213857	0.20218215893689626
+SRR10002942	Archaeal	2386	3.9741636059555156e-4
+SRR10002942	Viral	670	1.1159637954694869e-4
+SRR10002942	Human	1090658	0.18166191660285963
+SRR10002943	Filtered	248693	0.06953911076910688
+SRR10002943	Duplicate	193072	0.053986462000993204
+SRR10002943	Ribosomal	9934	0.002777728067860003
+SRR10002943	Unassigned	1748445	0.488897196658897
+SRR10002943	Bacterial	672552	0.18805783848352936
+SRR10002943	Archaeal	871	2.4354752839803327e-4
+SRR10002943	Viral	611	1.708467736523517e-4
+SRR10002943	Human	702126	0.19632726971756315
+SRR10002944	Filtered	315874	0.08496457769478771
+SRR10002944	Duplicate	204890	0.0551118240940535
+SRR10002944	Ribosomal	11453	0.0030806565540006576
+SRR10002944	Unassigned	1964058	0.5282972278125752
+SRR10002944	Bacterial	729585	0.19624559608404518
+SRR10002944	Archaeal	932	2.506916884945964e-4
+SRR10002944	Viral	459	1.2346296675860488e-4
+SRR10002944	Human	490463	0.1319259631052846
+SRR10002945	Filtered	422150	0.0818990570598675
+SRR10002945	Duplicate	446137	0.08655264626203507
+SRR10002945	Ribosomal	15432	0.0029938795417455296
+SRR10002945	Unassigned	1800631	0.3493307616078794
+SRR10002945	Bacterial	1564578	0.3035353852815667
+SRR10002945	Archaeal	2901	5.628074488467976e-4
+SRR10002945	Viral	991	1.922585942113673e-4
+SRR10002945	Human	901696	0.17493320420384764
+SRR10002946	Filtered	285468	0.05871925429912205
+SRR10002946	Duplicate	455073	0.0936061037022166
+SRR10002946	Ribosomal	14179	0.00291654513538208
+SRR10002946	Unassigned	1583440	0.32570521399036606
+SRR10002946	Bacterial	1419220	0.29192603054072613
+SRR10002946	Archaeal	1750	3.599657230353791e-4
+SRR10002946	Viral	1005	2.0672317237174626e-4
+SRR10002946	Human	1101439	0.22656016343677993
+SRR10002947	Filtered	568934	0.11977288090563012
+SRR10002947	Duplicate	383671	0.08077102263170072
+SRR10002947	Ribosomal	27927	0.005879235983526266
+SRR10002947	Unassigned	3100990	0.6528252942512663
+SRR10002947	Bacterial	579183	0.1219305165125754
+SRR10002947	Archaeal	725	1.5262814079767045e-4
+SRR10002947	Viral	203	4.2735879423347725e-5
+SRR10002947	Human	88474	0.018625685695080132
+SRR10002948	Filtered	273541	0.0742424201301151
+SRR10002948	Duplicate	223711	0.06071794008842616
+SRR10002948	Ribosomal	33726	0.00915365470371265
+SRR10002948	Unassigned	2825318	0.766826347630434
+SRR10002948	Bacterial	250751	0.06805693146565411
+SRR10002948	Archaeal	451	1.224069937548006e-4
+SRR10002948	Viral	165	4.478304649565876e-5
+SRR10002948	Human	76767	0.02083551594140749
+SRR10002949	Filtered	329194	0.06721634522225392
+SRR10002949	Duplicate	446208	0.09110880201015654
+SRR10002949	Ribosomal	9611	0.0019624181908876907
+SRR10002949	Unassigned	1463100	0.29874248830379563
+SRR10002949	Bacterial	1274992	0.2603337315613649
+SRR10002949	Archaeal	1727	3.526268042516951e-4
+SRR10002949	Viral	568	1.159768528170022e-4
+SRR10002949	Human	1372129	0.2801676110544726
+SRR10002950	Filtered	304665	0.05768804733727811
+SRR10002950	Duplicate	522823	0.09899607100591716
+SRR10002950	Ribosomal	10484	0.001985136094674556
+SRR10002950	Unassigned	1793821	0.3396584142011834
+SRR10002950	Bacterial	1260152	0.2386086627218935
+SRR10002950	Archaeal	3142	5.949349112426036e-4
+SRR10002950	Viral	448	8.48284023668639e-5
+SRR10002950	Human	1385715	0.2623839053254438
+SRR10002951	Filtered	311564	0.05351714945308635
+SRR10002951	Duplicate	559440	0.09609465178914968
+SRR10002951	Ribosomal	14980	0.0025731050404001537
+SRR10002951	Unassigned	1680161	0.288600182762601
+SRR10002951	Bacterial	1658169	0.2848226309569615
+SRR10002951	Archaeal	1308	2.2467432528994668e-4
+SRR10002951	Viral	1154	1.982218435662068e-4
+SRR10002951	Human	1594984	0.2739693838289452
+SRR10002952	Filtered	249270	0.05941646155628367
+SRR10002952	Duplicate	367726	0.08765185438378453
+SRR10002952	Ribosomal	9332	0.0022243929042533766
+SRR10002952	Unassigned	1020147	0.24316413931583472
+SRR10002952	Bacterial	835297	0.19910294896529498
+SRR10002952	Archaeal	620	1.4778435497611375e-4
+SRR10002952	Viral	419	9.987362054030913e-5
+SRR10002952	Human	1712491	0.4081925448990323
+SRR10002953	Filtered	389665	0.09628687714623255
+SRR10002953	Duplicate	370288	0.09149878784269606
+SRR10002953	Ribosomal	12398	0.003063566660744463
+SRR10002953	Unassigned	1774756	0.43854519378578805
+SRR10002953	Bacterial	1013855	0.2505252764017646
+SRR10002953	Archaeal	950	2.347465984600129e-4
+SRR10002953	Viral	600	1.4826100955369235e-4
+SRR10002953	Human	484405	0.11969729055476057
+SRR10002954	Filtered	355083	0.07048603749854099
+SRR10002954	Duplicate	462894	0.09188714706660028
+SRR10002954	Ribosomal	15311	0.0030393224123378506
+SRR10002954	Unassigned	2282476	0.4530847405409998
+SRR10002954	Bacterial	1419733	0.2818252450157177
+SRR10002954	Archaeal	1699	3.372613662440081e-4
+SRR10002954	Viral	558	1.1076624035559536e-4
+SRR10002954	Human	499882	0.0992294798592038
+SRR10002955	Filtered	442367	0.07324959891031438
+SRR10002955	Duplicate	557428	0.09230204201800478
+SRR10002955	Ribosomal	16003	0.00264986613233302
+SRR10002955	Unassigned	2876779	0.47635313643109745
+SRR10002955	Bacterial	1500324	0.24843202869002096
+SRR10002955	Archaeal	2142	3.5468432515511645e-4
+SRR10002955	Viral	667	1.1044558584428696e-4
+SRR10002955	Human	643463	0.10654819790723001
+SRR10002956	Filtered	309622	0.06113870648298713
+SRR10002956	Duplicate	500423	0.09881473188060239
+SRR10002956	Ribosomal	10948	0.0021618184708313464
+SRR10002956	Unassigned	1927317	0.38057266073687046
+SRR10002956	Bacterial	1522643	0.3006647572051565
+SRR10002956	Archaeal	1341	2.6479709256346687e-4
+SRR10002956	Viral	564	1.1136879955689435e-4
+SRR10002956	Human	791397	0.15627115933143176
+SRR10002957	Filtered	676383	0.1736845835394028
+SRR10002957	Duplicate	227877	0.05851525222131321
+SRR10002957	Ribosomal	11457	0.0029419785441250563
+SRR10002957	Unassigned	2163998	0.5556808663288412
+SRR10002957	Bacterial	450472	0.11567416939243277
+SRR10002957	Archaeal	1586	4.0726001317817394e-4
+SRR10002957	Viral	236	6.060111167090104e-5
+SRR10002957	Human	362309	0.09303528884903596
+SRR10002958	Filtered	449284	0.11183165264593455
+SRR10002958	Duplicate	231352	0.05758601798181607
+SRR10002958	Ribosomal	11517	0.002866706011171616
+SRR10002958	Unassigned	2019008	0.5025529539119199
+SRR10002958	Bacterial	537050	0.133677560415014
+SRR10002958	Archaeal	777	1.9340371369977818e-4
+SRR10002958	Viral	373	9.284373900903123e-5
+SRR10002958	Human	768142	0.19119886158143504
+SRR10002959	Filtered	303016	0.08348840784716532
+SRR10002959	Duplicate	221998	0.061165943597879344
+SRR10002959	Ribosomal	10915	0.0030073526534962164
+SRR10002959	Unassigned	1622304	0.44698490510101013
+SRR10002959	Bacterial	468343	0.1290400883001721
+SRR10002959	Archaeal	723	1.99204394729983e-4
+SRR10002959	Viral	323	8.899449446443223e-5
+SRR10002959	Human	1001816	0.2760251036110825
+SRR10002960	Filtered	485411	0.10519938450869057
+SRR10002960	Duplicate	252485	0.05471912790949677
+SRR10002960	Ribosomal	24147	0.0052331931862511375
+SRR10002960	Unassigned	2439960	0.5287937237224221
+SRR10002960	Bacterial	524249	0.11361644488752114
+SRR10002960	Archaeal	1337	2.8975770447748256e-4
+SRR10002960	Viral	452	9.795847600884227e-5
+SRR10002960	Human	886159	0.19205040960513198
+SRR10002961	Filtered	521944	0.11426719938700673
+SRR10002961	Duplicate	297989	0.06523758962289967
+SRR10002961	Ribosomal	14154	0.0030986809698429203
+SRR10002961	Unassigned	2142502	0.4690497509714849
+SRR10002961	Bacterial	648839	0.1420478353675223
+SRR10002961	Archaeal	988	2.1629905314432708e-4
+SRR10002961	Viral	577	1.263203984456242e-4
+SRR10002961	Human	940757	0.20595632422965354
+SRR10002962	Filtered	402515	0.10899327543320843
+SRR10002962	Duplicate	202259	0.0547678245428004
+SRR10002962	Ribosomal	11272	0.003052239545565073
+SRR10002962	Unassigned	1637348	0.4433621642522961
+SRR10002962	Bacterial	406779	0.11014788414703823
+SRR10002962	Archaeal	1351	3.658246651932589e-4
+SRR10002962	Viral	240	6.498735725120809e-5
+SRR10002962	Human	1031262	0.2792458000566473
+SRR10002963	Filtered	401895	0.09492217890561562
+SRR10002963	Duplicate	256670	0.06062199245998174
+SRR10002963	Ribosomal	14916	0.003522958037686865
+SRR10002963	Unassigned	2314339	0.5466156598271776
+SRR10002963	Bacterial	539807	0.12749513337688612
+SRR10002963	Archaeal	934	2.2059820375432634e-4
+SRR10002963	Viral	486	1.147866456366195e-4
+SRR10002963	Human	704895	0.1664866925432611
+SRR10002964	Filtered	416227	0.10617791938450392
+SRR10002964	Duplicate	210521	0.053703103755270924
+SRR10002964	Ribosomal	23156	0.005907007237078741
+SRR10002964	Unassigned	2699567	0.6886492401960159
+SRR10002964	Bacterial	462243	0.11791642538819262
+SRR10002964	Archaeal	567	1.4463953633717593e-4
+SRR10002964	Viral	201	5.127433298725285e-5
+SRR10002964	Human	107608	0.027450390169613453
+SRR10002965	Filtered	322445	0.07941249848228528
+SRR10002965	Duplicate	269236	0.06630806320884666
+SRR10002965	Ribosomal	12279	0.003024100447716606
+SRR10002965	Unassigned	1550935	0.3819678498150789
+SRR10002965	Bacterial	366026	0.09014572770387803
+SRR10002965	Archaeal	438	1.0787165046826886e-4
+SRR10002965	Viral	277	6.822019904043487e-5
+SRR10002965	Human	1538745	0.37896566849268576
+SRR10002966	Filtered	976156	0.23406008244503823
+SRR10002966	Duplicate	232668	0.055788512555700276
+SRR10002966	Ribosomal	13495	0.0032357951112279093
+SRR10002966	Unassigned	1757789	0.421477958708425
+SRR10002966	Bacterial	487677	0.11693389051191501
+SRR10002966	Archaeal	588	1.4098907190826312e-4
+SRR10002966	Viral	638	1.5297793856712901e-4
+SRR10002966	Human	701525	0.16820979365721817
+SRR10002967	Filtered	388074	0.10369533919545623
+SRR10002967	Duplicate	207678	0.05549261391753624
+SRR10002967	Ribosomal	7671	0.0020497300694412527
+SRR10002967	Unassigned	2163897	0.5782042430027009
+SRR10002967	Bacterial	605356	0.16175419057706675
+SRR10002967	Archaeal	1256	3.3560956423128843e-4
+SRR10002967	Viral	294	7.855828971655956e-5
+SRR10002967	Human	368218	0.09838971538385077
diff --git a/data/2024-04-12_leung/blast/putative-viral-best.blast.gz b/data/2024-04-12_leung/blast/putative-viral-best.blast.gz
new file mode 100644
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diff --git a/data/2024-04-12_leung/taxid-names.tsv.gz b/data/2024-04-12_leung/taxid-names.tsv.gz
new file mode 120000
index 0000000..626546b
--- /dev/null
+++ b/data/2024-04-12_leung/taxid-names.tsv.gz
@@ -0,0 +1 @@
+../2024-04-01_spurbeck/taxid-names.tsv.gz
\ No newline at end of file
diff --git a/data/2024-04-12_leung/viral-taxids.tsv.gz b/data/2024-04-12_leung/viral-taxids.tsv.gz
new file mode 120000
index 0000000..349083e
--- /dev/null
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+../2024-03-19_brumfield/viral-taxids.tsv.gz
\ No newline at end of file
diff --git a/docs/index.html b/docs/index.html
index 84cd8a9..41646c0 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -164,7 +164,7 @@
 
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new file mode 100644
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+      <div class="quarto-title-block"><div><h1 class="title">Workflow analysis of Leung et al.&nbsp;(2021)</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
+            <p class="subtitle lead">Air sampling from a student dorm in Colorado.</p>
+                      </div>
+  </div>
+    
+  
+  <div class="quarto-title-meta">
+
+      <div>
+      <div class="quarto-title-meta-heading">Author</div>
+      <div class="quarto-title-meta-contents">
+               <p>Will Bradshaw </p>
+            </div>
+    </div>
+      
+      <div>
+      <div class="quarto-title-meta-heading">Published</div>
+      <div class="quarto-title-meta-contents">
+        <p class="date">April 12, 2024</p>
+      </div>
+    </div>
+    
+      
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+<main class="content quarto-banner-title-block" id="quarto-document-content"><p>The last in our current run of air sampling datasets is <a href="https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-021-01044-7">Leung et al.</a> (2021), a study of active air samples collected in public transit systems from six cities (Denver, Hong Kong, London, NYC, Oslo, Stockholm) from June to September 2017.</p>
+<p>Samples from Denver originated from their rail and bus system; all other samples originated from metro systems. Collection took place during working days and working hours. Air samples were collected with the SASS 3100 Dry Air Samplers (filtration) for 30 min at a flowrate of 300 L/min using electret microfibrous filters. Filters were stationed at 1.5m above floor level, facing downward (to avoid direct deposition).</p>
+<p>This was a DNA-sequencing study, focused on the bacterial microbiome and resistome. Sample processing followed an ideosyncratic protocol, where samples were pelleted and the pellet and supernatant were processed separately before being recombined for NA extraction and sequencing; I don’t have a great understanding of how this is expected to affect the viral fraction. Samples were sequenced with Illumina HiSeqX 2x150bp.</p>
+<section id="the-raw-data" class="level1"><h1>The raw data</h1>
+<p>In total, the Leung dataset comprised 293 samples:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb1"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Importing the data is a bit more complicated this time as the samples are split across three pipeline runs</span></span>
+<span><span class="va">data_dir_base</span> <span class="op">&lt;-</span> <span class="st">"../data/2024-04-12_leung"</span></span>
+<span><span class="va">data_dirs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste</a></span><span class="op">(</span><span class="va">data_dir_base</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, sep<span class="op">=</span><span class="st">"/"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Define geo relationships for filling in</span></span>
+<span><span class="va">geo</span> <span class="op">&lt;-</span> <span class="fu">tribble</span><span class="op">(</span><span class="op">~</span><span class="va">region</span>, <span class="op">~</span><span class="va">country</span>, <span class="op">~</span><span class="va">city</span>,</span>
+<span>               <span class="st">"Asia"</span>, <span class="st">"Hong Kong"</span>, <span class="st">"Hong Kong"</span>,</span>
+<span>               <span class="st">"Europe"</span>, <span class="st">"Norway"</span>, <span class="st">"Oslo"</span>,</span>
+<span>               <span class="st">"Europe"</span>, <span class="st">"Sweden"</span>, <span class="st">"Stockholm"</span>,</span>
+<span>               <span class="st">"Europe"</span>, <span class="st">"United Kingdom"</span>, <span class="st">"London"</span>,</span>
+<span>               <span class="st">"North America"</span>, <span class="st">"USA"</span>, <span class="st">"New York City"</span>,</span>
+<span>               <span class="st">"North America"</span>, <span class="st">"USA"</span>, <span class="st">"Denver"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Data input paths</span></span>
+<span><span class="va">libraries_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dirs</span>, <span class="st">"sample-metadata.csv"</span><span class="op">)</span></span>
+<span><span class="va">basic_stats_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dirs</span>, <span class="st">"qc_basic_stats.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">adapter_stats_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dirs</span>, <span class="st">"qc_adapter_stats.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">quality_base_stats_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dirs</span>, <span class="st">"qc_quality_base_stats.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">quality_seq_stats_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dirs</span>, <span class="st">"qc_quality_sequence_stats.tsv.gz"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Import libraries and extract metadata from sample names</span></span>
+<span><span class="va">libraries_raw</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">libraries_paths</span>, <span class="va">read_csv</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="va">bind_rows</span></span>
+<span><span class="va">libraries</span> <span class="op">&lt;-</span> <span class="va">libraries_raw</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="co"># Fix missing entries</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>city <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">city</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/grep.html">sub</a></span><span class="op">(</span><span class="st">", .*"</span>, <span class="st">""</span>, <span class="va">location</span><span class="op">)</span>, <span class="va">city</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">geo</span>, by<span class="op">=</span><span class="st">"city"</span>, suffix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">""</span>, <span class="st">"_new"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>region <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">region</span> <span class="op">==</span> <span class="st">"uncalculated"</span>, <span class="va">region_new</span>, <span class="va">region</span><span class="op">)</span>,</span>
+<span>         country <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">country</span> <span class="op">==</span> <span class="st">"uncalculated"</span>, <span class="va">country_new</span>, <span class="va">country</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="op">-</span><span class="va">country_new</span>, <span class="op">-</span><span class="va">region_new</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="co"># Add sample aliases</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="va">city</span>, <span class="va">date</span>, <span class="va">location</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">city</span>, <span class="va">date</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>sample_count <span class="op">=</span> <span class="fu">row_number</span><span class="op">(</span><span class="op">)</span>,</span>
+<span>         date_alias <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">date</span><span class="op">)</span>, <span class="va">sample_count</span>, sep<span class="op">=</span><span class="st">"_"</span><span class="op">)</span>,</span>
+<span>         sample_alias <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste</a></span><span class="op">(</span><span class="va">city</span>, <span class="va">date_alias</span>, sep<span class="op">=</span><span class="st">"_"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">count_city</span> <span class="op">&lt;-</span> <span class="va">libraries</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">region</span>, <span class="va">country</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">count</span><span class="op">(</span>name<span class="op">=</span><span class="st">"n_samples"</span><span class="op">)</span></span>
+<span><span class="va">count_city</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div data-pagedtable="false">
+  <script data-pagedtable-source="" type="application/json">
+{"columns":[{"label":["region"],"name":[1],"type":["chr"],"align":["left"]},{"label":["country"],"name":[2],"type":["chr"],"align":["left"]},{"label":["city"],"name":[3],"type":["chr"],"align":["left"]},{"label":["n_samples"],"name":[4],"type":["int"],"align":["right"]}],"data":[{"1":"Asia","2":"Hong Kong","3":"Hong Kong","4":"81"},{"1":"Europe","2":"Norway","3":"Oslo","4":"79"},{"1":"Europe","2":"Sweden","3":"Stockholm","4":"32"},{"1":"Europe","2":"United Kingdom","3":"London","4":"41"},{"1":"North America","2":"USA","3":"Denver","4":"31"},{"1":"North America","2":"USA","3":"New York City","4":"29"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+  </script>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Import QC data</span></span>
+<span><span class="va">stages</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"raw_concat"</span>, <span class="st">"cleaned"</span>, <span class="st">"dedup"</span>, <span class="st">"ribo_initial"</span>, <span class="st">"ribo_secondary"</span><span class="op">)</span></span>
+<span><span class="va">import_basic</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">paths</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">paths</span>, <span class="va">read_tsv</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>stage <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">stage</span>, levels <span class="op">=</span> <span class="va">stages</span><span class="op">)</span>,</span>
+<span>           sample <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">import_basic_paired</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">paths</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="fu">import_basic</span><span class="op">(</span><span class="va">paths</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="va">read_pair</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>read_pair <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">basic_stats</span> <span class="op">&lt;-</span> <span class="fu">import_basic</span><span class="op">(</span><span class="va">basic_stats_paths</span><span class="op">)</span></span>
+<span><span class="va">adapter_stats</span> <span class="op">&lt;-</span> <span class="fu">import_basic_paired</span><span class="op">(</span><span class="va">adapter_stats_paths</span><span class="op">)</span></span>
+<span><span class="va">quality_base_stats</span> <span class="op">&lt;-</span> <span class="fu">import_basic_paired</span><span class="op">(</span><span class="va">quality_base_stats_paths</span><span class="op">)</span></span>
+<span><span class="va">quality_seq_stats</span> <span class="op">&lt;-</span> <span class="fu">import_basic_paired</span><span class="op">(</span><span class="va">quality_seq_stats_paths</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Filter to raw data</span></span>
+<span><span class="va">basic_stats_raw</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span><span class="va">adapter_stats_raw</span> <span class="op">&lt;-</span> <span class="va">adapter_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span><span class="va">quality_base_stats_raw</span> <span class="op">&lt;-</span> <span class="va">quality_base_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span><span class="va">quality_seq_stats_raw</span> <span class="op">&lt;-</span> <span class="va">quality_seq_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get key values for readout</span></span>
+<span><span class="va">raw_read_counts</span> <span class="op">&lt;-</span> <span class="va">basic_stats_raw</span> <span class="op">%&gt;%</span> <span class="va">ungroup</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>rmin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>, rmax<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>,</span>
+<span>            rmean<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>, </span>
+<span>            rtot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>,</span>
+<span>            btot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_bases_approx</span><span class="op">)</span>,</span>
+<span>            dmin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>, dmax<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>,</span>
+<span>            dmean<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<p>These 293 samples yielded 0.39M-7.86M (mean 4.57M) reads per sample, for a total of 1.34B read pairs (402 gigabases of sequence). Read qualities were high at the 5’ end but dropped off significantly in some samples, in definite need of cleaning. Adapter levels were high. With the exception of a couple of early samples, inferred duplication levels were low (mean 9.4%).</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb3"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Prepare data</span></span>
+<span><span class="va">basic_stats_raw_metrics</span> <span class="op">&lt;-</span> <span class="va">basic_stats_raw</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">city</span>, <span class="va">date</span>,</span>
+<span>         `# Read pairs` <span class="op">=</span> <span class="va">n_read_pairs</span>,</span>
+<span>         `Total base pairs\n(approx)` <span class="op">=</span> <span class="va">n_bases_approx</span>,</span>
+<span>         `% Duplicates\n(FASTQC)` <span class="op">=</span> <span class="va">percent_duplicates</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_longer</span><span class="op">(</span><span class="op">-</span><span class="op">(</span><span class="va">sample</span><span class="op">:</span><span class="va">date</span><span class="op">)</span>, names_to <span class="op">=</span> <span class="st">"metric"</span>, values_to <span class="op">=</span> <span class="st">"value"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>metric <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">metric</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Set up plot templates</span></span>
+<span><span class="va">scale_fill_city</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_fill_brewer</span>, palette<span class="op">=</span><span class="st">"Set1"</span>,</span>
+<span>                                  name<span class="op">=</span><span class="st">"City"</span><span class="op">)</span></span>
+<span><span class="va">scale_x_cdate</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_x_date</span>, name<span class="op">=</span><span class="st">"Collection Date"</span>,</span>
+<span>                                date_breaks <span class="op">=</span> <span class="st">"1 month"</span>, date_labels <span class="op">=</span> <span class="st">"%Y-%m-%d"</span><span class="op">)</span></span>
+<span><span class="va">g_basic</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">basic_stats_raw_metrics</span>, </span>
+<span>                  <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">date</span>, y<span class="op">=</span><span class="va">value</span>, fill<span class="op">=</span><span class="va">city</span>, group<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/interaction.html">interaction</a></span><span class="op">(</span><span class="va">city</span>,<span class="va">sample</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position <span class="op">=</span> <span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_cdate</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">expand_limits</span><span class="op">(</span>y<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_city</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">metric</span><span class="op">~</span><span class="va">.</span>, scales <span class="op">=</span> <span class="st">"free"</span>, space<span class="op">=</span><span class="st">"free_x"</span>, switch<span class="op">=</span><span class="st">"y"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_rotate</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span></span>
+<span>    axis.title.y <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span>,</span>
+<span>    strip.text.y <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span></span>
+<span>  <span class="op">)</span></span>
+<span><span class="va">g_basic</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-basic-stats-1.png" class="img-fluid figure-img" width="864"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb4"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Set up plotting templates</span></span>
+<span><span class="va">scale_color_city</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_color_brewer</span>, palette<span class="op">=</span><span class="st">"Set1"</span>,</span>
+<span>                                   name<span class="op">=</span><span class="st">"City"</span><span class="op">)</span></span>
+<span><span class="va">g_qual_raw</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span>mapping<span class="op">=</span><span class="fu">aes</span><span class="op">(</span>color<span class="op">=</span><span class="va">city</span>, linetype<span class="op">=</span><span class="va">read_pair</span>, </span>
+<span>                         group<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/interaction.html">interaction</a></span><span class="op">(</span><span class="va">sample</span>,<span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_color_city</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_linetype_discrete</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Read Pair"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>color<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>,</span>
+<span>         linetype <span class="op">=</span> <span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span></span>
+<span><span class="co"># Visualize adapters</span></span>
+<span><span class="va">g_adapters_raw</span> <span class="op">&lt;-</span> <span class="va">g_qual_raw</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">pc_adapters</span><span class="op">)</span>, data<span class="op">=</span><span class="va">adapter_stats_raw</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Adapters"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">.</span><span class="op">~</span><span class="va">adapter</span><span class="op">)</span></span>
+<span><span class="va">g_adapters_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-raw-quality-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb5"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Visualize quality</span></span>
+<span><span class="va">g_quality_base_raw</span> <span class="op">&lt;-</span> <span class="va">g_qual_raw</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">mean_phred_score</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_base_stats_raw</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">10</span>,<span class="fl">45</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_quality_base_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-raw-quality-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb6"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_quality_seq_raw</span> <span class="op">&lt;-</span> <span class="va">g_qual_raw</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">mean_phred_score</span>, y<span class="op">=</span><span class="va">n_sequences</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_seq_stats_raw</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Sequences"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_quality_seq_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-raw-quality-3.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="preprocessing" class="level1"><h1>Preprocessing</h1>
+<p>The average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. Read loss during cleaning was highly variable but averaged 11%, with a further ~7% lost during deduplication and ~0.3% during ribodepletion.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb7"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">n_reads_rel</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">city</span>, <span class="va">stage</span>, </span>
+<span>         <span class="va">percent_duplicates</span>, <span class="va">n_read_pairs</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="va">sample</span>, <span class="va">stage</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_retained <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">n_read_pairs</span> <span class="op">/</span> <span class="fu"><a href="https://rdrr.io/r/stats/lag.html">lag</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>, <span class="fl">0</span><span class="op">)</span>,</span>
+<span>         p_reads_lost <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">p_reads_retained</span>,</span>
+<span>         p_reads_retained_abs <span class="op">=</span> <span class="va">n_read_pairs</span> <span class="op">/</span> <span class="va">n_read_pairs</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
+<span>         p_reads_lost_abs <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_retained_abs</span>,</span>
+<span>         p_reads_lost_abs_marginal <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">p_reads_lost_abs</span> <span class="op">-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lag.html">lag</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">)</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">n_reads_rel_display</span> <span class="op">&lt;-</span> <span class="va">n_reads_rel</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">rename</span><span class="op">(</span>Stage<span class="op">=</span><span class="va">stage</span>, City<span class="op">=</span><span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">Stage</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>`% Total Reads Lost (Cumulative)` <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">"-"</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">" (mean "</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">")"</span><span class="op">)</span>,</span>
+<span>            `% Total Reads Lost (Marginal)` <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">"-"</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">" (mean "</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">")"</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">Stage</span> <span class="op">!=</span> <span class="st">"raw_concat"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>Stage <span class="op">=</span> <span class="va">Stage</span> <span class="op">%&gt;%</span> <span class="va">as.numeric</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span>labels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Trimming &amp; filtering"</span>, <span class="st">"Deduplication"</span>, <span class="st">"Initial ribodepletion"</span>, <span class="st">"Secondary ribodepletion"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">n_reads_rel_display</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div data-pagedtable="false">
+  <script data-pagedtable-source="" type="application/json">
+{"columns":[{"label":["Stage"],"name":[1],"type":["fct"],"align":["left"]},{"label":["% Total Reads Lost (Cumulative)"],"name":[2],"type":["chr"],"align":["left"]},{"label":["% Total Reads Lost (Marginal)"],"name":[3],"type":["chr"],"align":["left"]}],"data":[{"1":"Trimming & filtering","2":"4.8-88.7 (mean 11)","3":"4.8-88.7 (mean 11)"},{"1":"Deduplication","2":"11.5-89.9 (mean 17.8)","3":"1.2-11 (mean 6.8)"},{"1":"Initial ribodepletion","2":"11.7-89.9 (mean 18.1)","3":"0-0.7 (mean 0.2)"},{"1":"Secondary ribodepletion","2":"11.8-89.9 (mean 18.1)","3":"0-0.4 (mean 0.1)"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+  </script>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb8"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_stage_trace</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">basic_stats</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">stage</span>, color<span class="op">=</span><span class="va">city</span>, group<span class="op">=</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_city</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">city</span>, scales<span class="op">=</span><span class="st">"free"</span>, ncol<span class="op">=</span><span class="fl">3</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot reads over preprocessing</span></span>
+<span><span class="va">g_reads_stages</span> <span class="op">&lt;-</span> <span class="va">g_stage_trace</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">n_read_pairs</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"# Read pairs"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_reads_stages</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/preproc-figures-1.png" class="img-fluid figure-img" width="576"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb9"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Plot relative read losses during preprocessing</span></span>
+<span><span class="va">g_reads_rel</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">n_reads_rel</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">stage</span>, color<span class="op">=</span><span class="va">city</span>, group<span class="op">=</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"% Total Reads Lost"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, </span>
+<span>                     labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_city</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">city</span>, scales<span class="op">=</span><span class="st">"free"</span>, ncol<span class="op">=</span><span class="fl">3</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span></span>
+<span><span class="va">g_reads_rel</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/preproc-figures-2.png" class="img-fluid figure-img" width="576"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>Data cleaning was very successful at removing adapters and improving read qualities:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb10"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_qual</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span>mapping<span class="op">=</span><span class="fu">aes</span><span class="op">(</span>color<span class="op">=</span><span class="va">city</span>, linetype<span class="op">=</span><span class="va">read_pair</span>, </span>
+<span>                         group<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/interaction.html">interaction</a></span><span class="op">(</span><span class="va">sample</span>,<span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_color_city</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_linetype_discrete</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Read Pair"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>color<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>,</span>
+<span>         linetype <span class="op">=</span> <span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span></span>
+<span><span class="co"># Visualize adapters</span></span>
+<span><span class="va">g_adapters</span> <span class="op">&lt;-</span> <span class="va">g_qual</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">pc_adapters</span><span class="op">)</span>, data<span class="op">=</span><span class="va">adapter_stats</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Adapters"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">20</span><span class="op">)</span>,</span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">50</span>,<span class="fl">10</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">stage</span><span class="op">~</span><span class="va">adapter</span><span class="op">)</span></span>
+<span><span class="va">g_adapters</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-quality-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb11"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Visualize quality</span></span>
+<span><span class="va">g_quality_base</span> <span class="op">&lt;-</span> <span class="va">g_qual</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">mean_phred_score</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_base_stats</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">10</span>,<span class="fl">45</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">stage</span><span class="op">~</span><span class="va">.</span><span class="op">)</span></span>
+<span><span class="va">g_quality_base</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-quality-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb12"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_quality_seq</span> <span class="op">&lt;-</span> <span class="va">g_qual</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">mean_phred_score</span>, y<span class="op">=</span><span class="va">n_sequences</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_seq_stats</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Sequences"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">stage</span><span class="op">~</span><span class="va">.</span><span class="op">)</span></span>
+<span><span class="va">g_quality_seq</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-quality-3.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>According to FASTQC, cleaning + deduplication was very effective at reducing measured duplicate levels, which fell from an average of 9.4% to 1.7% for DNA reads:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb13"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">stage_dup</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">stage</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>dmin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>, dmax<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>,</span>
+<span>            dmean<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_dup_stages</span> <span class="op">&lt;-</span> <span class="va">g_stage_trace</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">percent_duplicates</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"% Duplicates"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_dup_stages</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/preproc-dedup-1.png" class="img-fluid figure-img" width="576"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb14"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_readlen_stages</span> <span class="op">&lt;-</span> <span class="va">g_stage_trace</span> <span class="op">+</span> <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">mean_seq_len</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"Mean read length (nt)"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_readlen_stages</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/preproc-dedup-2.png" class="img-fluid figure-img" width="576"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="high-level-composition" class="level1"><h1>High-level composition</h1>
+<p>As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb15"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Import Bracken data</span></span>
+<span><span class="va">bracken_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dirs</span>, <span class="st">"bracken_counts.tsv"</span><span class="op">)</span></span>
+<span><span class="va">bracken</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">bracken_paths</span>, <span class="va">read_tsv</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span></span>
+<span><span class="va">total_assigned</span> <span class="op">&lt;-</span> <span class="va">bracken</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">summarize</span><span class="op">(</span></span>
+<span>  name <span class="op">=</span> <span class="st">"Total"</span>,</span>
+<span>  kraken_assigned_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">kraken_assigned_reads</span><span class="op">)</span>,</span>
+<span>  added_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">added_reads</span><span class="op">)</span>,</span>
+<span>  new_est_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">new_est_reads</span><span class="op">)</span>,</span>
+<span>  fraction_total_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">fraction_total_reads</span><span class="op">)</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">bracken_spread</span> <span class="op">&lt;-</span> <span class="va">bracken</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">sample</span>, <span class="va">new_est_reads</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/chartr.html">tolower</a></span><span class="op">(</span><span class="va">name</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_wider</span><span class="op">(</span>id_cols <span class="op">=</span> <span class="st">"sample"</span>, names_from <span class="op">=</span> <span class="st">"name"</span>, </span>
+<span>              values_from <span class="op">=</span> <span class="st">"new_est_reads"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count reads</span></span>
+<span><span class="va">read_counts_preproc</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">stage</span>, <span class="va">n_read_pairs</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_wider</span><span class="op">(</span>id_cols <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"sample"</span>, <span class="st">"date"</span>, <span class="st">"date_alias"</span>, <span class="st">"city"</span><span class="op">)</span>,</span>
+<span>              names_from<span class="op">=</span><span class="st">"stage"</span>, values_from<span class="op">=</span><span class="st">"n_read_pairs"</span><span class="op">)</span></span>
+<span><span class="va">read_counts</span> <span class="op">&lt;-</span> <span class="va">read_counts_preproc</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">total_assigned</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">new_est_reads</span><span class="op">)</span>, by <span class="op">=</span> <span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>assigned <span class="op">=</span> <span class="va">new_est_reads</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">bracken_spread</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Assess composition</span></span>
+<span><span class="va">read_comp</span> <span class="op">&lt;-</span> <span class="fu">transmute</span><span class="op">(</span><span class="va">read_counts</span>, <span class="va">sample</span>, <span class="va">date</span>, <span class="va">date_alias</span>, <span class="va">city</span>,</span>
+<span>                       n_filtered <span class="op">=</span> <span class="va">raw_concat</span><span class="op">-</span><span class="va">cleaned</span>,</span>
+<span>                       n_duplicate <span class="op">=</span> <span class="va">cleaned</span><span class="op">-</span><span class="va">dedup</span>,</span>
+<span>                       n_ribosomal <span class="op">=</span> <span class="op">(</span><span class="va">dedup</span><span class="op">-</span><span class="va">ribo_initial</span><span class="op">)</span> <span class="op">+</span> <span class="op">(</span><span class="va">ribo_initial</span><span class="op">-</span><span class="va">ribo_secondary</span><span class="op">)</span>,</span>
+<span>                       n_unassigned <span class="op">=</span> <span class="va">ribo_secondary</span><span class="op">-</span><span class="va">assigned</span>,</span>
+<span>                       n_bacterial <span class="op">=</span> <span class="va">bacteria</span>,</span>
+<span>                       n_archaeal <span class="op">=</span> <span class="va">archaea</span>,</span>
+<span>                       n_viral <span class="op">=</span> <span class="va">viruses</span>,</span>
+<span>                       n_human <span class="op">=</span> <span class="va">eukaryota</span><span class="op">)</span></span>
+<span><span class="va">read_comp_long</span> <span class="op">&lt;-</span> <span class="fu">pivot_longer</span><span class="op">(</span><span class="va">read_comp</span>, <span class="op">-</span><span class="op">(</span><span class="va">sample</span><span class="op">:</span><span class="va">city</span><span class="op">)</span>, </span>
+<span>                               names_to <span class="op">=</span> <span class="st">"classification"</span>,</span>
+<span>                               names_prefix <span class="op">=</span> <span class="st">"n_"</span>, values_to <span class="op">=</span> <span class="st">"n_reads"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>classification <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu">str_to_sentence</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">n_reads</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Summarize composition</span></span>
+<span><span class="va">read_comp_summ</span> <span class="op">&lt;-</span> <span class="va">read_comp_long</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">city</span>, <span class="va">classification</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop_last"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>n_reads <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">n_reads</span>,<span class="fl">0</span><span class="op">)</span>,</span>
+<span>    p_reads <span class="op">=</span> <span class="va">n_reads</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads</span><span class="op">)</span>,</span>
+<span>    pc_reads <span class="op">=</span> <span class="va">p_reads</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb16"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Prepare plotting templates</span></span>
+<span><span class="va">g_comp_base</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span>mapping<span class="op">=</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">date_alias</span>, y<span class="op">=</span><span class="va">p_reads</span>, fill<span class="op">=</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_discrete</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Collection Date"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">city</span>, scales <span class="op">=</span> <span class="st">"free"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>axis.text.x <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">scale_y_pc_reads</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_y_continuous</span>, name <span class="op">=</span> <span class="st">"% Reads"</span>,</span>
+<span>                                   expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot overall composition</span></span>
+<span><span class="va">g_comp</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> <span class="fu">geom_col</span><span class="op">(</span>data <span class="op">=</span> <span class="va">read_comp_long</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_pc_reads</span><span class="op">(</span>limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Set1"</span>, name <span class="op">=</span> <span class="st">"Classification"</span><span class="op">)</span></span>
+<span><span class="va">g_comp</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-composition-all-1.png" class="img-fluid figure-img" width="768"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb17"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Plot composition of minor components</span></span>
+<span><span class="va">read_comp_minor</span> <span class="op">&lt;-</span> <span class="va">read_comp_long</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">classification</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Archaeal"</span>, <span class="st">"Viral"</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">palette_minor</span> <span class="op">&lt;-</span> <span class="fu">brewer.pal</span><span class="op">(</span><span class="fl">9</span>, <span class="st">"Set1"</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">6</span>,<span class="fl">7</span>,<span class="fl">9</span><span class="op">)</span><span class="op">]</span></span>
+<span><span class="va">g_comp_minor</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">read_comp_minor</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_pc_reads</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_minor</span>, name <span class="op">=</span> <span class="st">"Classification"</span><span class="op">)</span></span>
+<span><span class="va">g_comp_minor</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-composition-all-2.png" class="img-fluid figure-img" width="768"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb18"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">p_reads_summ_group</span> <span class="op">&lt;-</span> <span class="va">read_comp_long</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>classification <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">classification</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Filtered"</span>, <span class="st">"Duplicate"</span>, <span class="st">"Unassigned"</span><span class="op">)</span>, <span class="st">"Excluded"</span>, <span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>         classification <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">classification</span>, <span class="va">sample</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">classification</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>pc_min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span><span class="op">*</span><span class="fl">100</span>, pc_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span><span class="op">*</span><span class="fl">100</span>, </span>
+<span>            pc_mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span><span class="op">*</span><span class="fl">100</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span><span class="va">p_reads_summ_prep</span> <span class="op">&lt;-</span> <span class="va">p_reads_summ_group</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>classification <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span>,</span>
+<span>         pc_min <span class="op">=</span> <span class="va">pc_min</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html">signif</a></span><span class="op">(</span>digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">format</span>, scientific<span class="op">=</span><span class="cn">FALSE</span>, trim<span class="op">=</span><span class="cn">TRUE</span>, digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>         pc_max <span class="op">=</span> <span class="va">pc_max</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html">signif</a></span><span class="op">(</span>digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">format</span>, scientific<span class="op">=</span><span class="cn">FALSE</span>, trim<span class="op">=</span><span class="cn">TRUE</span>, digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>         pc_mean <span class="op">=</span> <span class="va">pc_mean</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html">signif</a></span><span class="op">(</span>digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">format</span>, scientific<span class="op">=</span><span class="cn">FALSE</span>, trim<span class="op">=</span><span class="cn">TRUE</span>, digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>         display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">pc_min</span>, <span class="st">"-"</span>, <span class="va">pc_max</span>, <span class="st">"% (mean "</span>, <span class="va">pc_mean</span>, <span class="st">"%)"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">p_reads_summ</span> <span class="op">&lt;-</span> <span class="va">p_reads_summ_prep</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">city</span>, <span class="va">classification</span>, read_fraction<span class="op">=</span><span class="va">display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="va">city</span>, <span class="va">classification</span><span class="op">)</span></span>
+<span><span class="va">p_reads_summ</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div data-pagedtable="false">
+  <script data-pagedtable-source="" type="application/json">
+{"columns":[{"label":["city"],"name":[1],"type":["chr"],"align":["left"]},{"label":["classification"],"name":[2],"type":["fct"],"align":["left"]},{"label":["read_fraction"],"name":[3],"type":["chr"],"align":["left"]}],"data":[{"1":"Denver","2":"Excluded","3":"29-90% (mean 68%)"},{"1":"Denver","2":"Ribosomal","3":"0.13-0.92% (mean 0.41%)"},{"1":"Denver","2":"Bacterial","3":"6.8-22% (mean 14%)"},{"1":"Denver","2":"Archaeal","3":"0.0038-0.04% (mean 0.019%)"},{"1":"Denver","2":"Viral","3":"0.004-0.027% (mean 0.011%)"},{"1":"Denver","2":"Human","3":"0.83-63% (mean 17%)"},{"1":"Hong Kong","2":"Excluded","3":"20-85% (mean 47%)"},{"1":"Hong Kong","2":"Ribosomal","3":"0.094-0.85% (mean 0.25%)"},{"1":"Hong Kong","2":"Bacterial","3":"2-72% (mean 23%)"},{"1":"Hong Kong","2":"Archaeal","3":"0.0011-0.17% (mean 0.032%)"},{"1":"Hong Kong","2":"Viral","3":"0.0014-0.042% (mean 0.017%)"},{"1":"Hong Kong","2":"Human","3":"1.1-78% (mean 29%)"},{"1":"London","2":"Excluded","3":"55-78% (mean 65%)"},{"1":"London","2":"Ribosomal","3":"0.24-0.49% (mean 0.32%)"},{"1":"London","2":"Bacterial","3":"15-22% (mean 19%)"},{"1":"London","2":"Archaeal","3":"0.016-0.12% (mean 0.036%)"},{"1":"London","2":"Viral","3":"0.0068-0.019% (mean 0.011%)"},{"1":"London","2":"Human","3":"5.4-28% (mean 15%)"},{"1":"New York City","2":"Excluded","3":"43-73% (mean 63%)"},{"1":"New York City","2":"Ribosomal","3":"0.15-0.36% (mean 0.27%)"},{"1":"New York City","2":"Bacterial","3":"12-27% (mean 19%)"},{"1":"New York City","2":"Archaeal","3":"0.0091-0.081% (mean 0.04%)"},{"1":"New York City","2":"Viral","3":"0.0053-0.025% (mean 0.013%)"},{"1":"New York City","2":"Human","3":"1.8-45% (mean 17%)"},{"1":"Oslo","2":"Excluded","3":"44-92% (mean 71%)"},{"1":"Oslo","2":"Ribosomal","3":"0.081-0.59% (mean 0.31%)"},{"1":"Oslo","2":"Bacterial","3":"5.8-37% (mean 15%)"},{"1":"Oslo","2":"Archaeal","3":"0.00042-0.13% (mean 0.036%)"},{"1":"Oslo","2":"Viral","3":"0.0034-0.036% (mean 0.0092%)"},{"1":"Oslo","2":"Human","3":"0.84-49% (mean 14%)"},{"1":"Stockholm","2":"Excluded","3":"28-78% (mean 65%)"},{"1":"Stockholm","2":"Ribosomal","3":"0.23-0.95% (mean 0.3%)"},{"1":"Stockholm","2":"Bacterial","3":"9.5-70% (mean 15%)"},{"1":"Stockholm","2":"Archaeal","3":"0.0014-0.45% (mean 0.042%)"},{"1":"Stockholm","2":"Viral","3":"0.0073-0.044% (mean 0.014%)"},{"1":"Stockholm","2":"Human","3":"0.94-31% (mean 19%)"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+  </script>
+</div>
+</div>
+</div>
+<p>In many respects, these resemble the Prussin data: high human fraction (mean 19.6%), high bacterial fraction (mean 18.2%), high unclassified fraction (mean 43.9%), low viral fraction (mean 0.01%). One notable difference is that archaeal reads are more abundant (0.034% compared to 0.016% for Prussin).</p>
+<p>As in Prussin, viral DNA reads were dominated by <em>Caudoviricetes</em> phages. Other viral classes that are prominent in at least some samples include <em>Herviviricetes</em> (herpesviruses), <em>Papovaviricetes</em> (polyomaviruses and papillomaviruses), <em>Revtraviricetes</em> (retroviruses + Hep B), and <em>Naldaviricetes</em> (mainly arthropod viruses). I’ll investigate the first three of this latter group in more depth, restricting in each case to samples where that family makes up at least 5% of viral reads.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb19"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get viral taxonomy</span></span>
+<span><span class="va">viral_taxa_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir_base</span>, <span class="st">"viral-taxids.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">viral_taxa</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">viral_taxa_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get paths to Kraken reports</span></span>
+<span><span class="va">samples</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">basic_stats_raw</span><span class="op">$</span><span class="va">sample</span><span class="op">)</span></span>
+<span><span class="va">report_dirs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dirs</span>, <span class="st">"kraken"</span><span class="op">)</span></span>
+<span><span class="va">report_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">report_dirs</span>, <span class="va">list.files</span>, full.names <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">unlist</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">report_paths</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">str_extract</span><span class="op">(</span><span class="va">report_paths</span>, <span class="st">"SRR\\d*"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Extract viral taxa</span></span>
+<span><span class="va">col_names</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"pc_reads_total"</span>, <span class="st">"n_reads_clade"</span>, <span class="st">"n_reads_direct"</span>,</span>
+<span>               <span class="st">"rank"</span>, <span class="st">"taxid"</span>, <span class="st">"name"</span><span class="op">)</span></span>
+<span><span class="va">kraken_reports_raw</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">report_paths</span>, <span class="va">read_tsv</span>, col_names <span class="op">=</span> <span class="va">col_names</span>,</span>
+<span>                             show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span><span class="va">kraken_reports</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">kraken_reports_raw</span><span class="op">)</span>, </span>
+<span>                         <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">kraken_reports_raw</span><span class="op">[[</span><span class="va">x</span><span class="op">]</span><span class="op">]</span> <span class="op">%&gt;%</span> </span>
+<span>                           <span class="fu">mutate</span><span class="op">(</span>sample <span class="op">=</span> <span class="va">x</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span></span>
+<span><span class="va">kraken_reports_viral</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">kraken_reports</span>, <span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_viral <span class="op">=</span> <span class="va">n_reads_clade</span><span class="op">/</span><span class="va">n_reads_clade</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span><span class="va">kraken_reports_viral_cleaned</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="op">-</span><span class="va">pc_reads_total</span>, <span class="op">-</span><span class="va">n_reads_direct</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">p_reads_viral</span>, <span class="va">n_reads_clade</span>, <span class="fu">everything</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">viral_classes</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral_cleaned</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"C"</span><span class="op">)</span></span>
+<span><span class="va">viral_families</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral_cleaned</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"F"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb20"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">major_threshold</span> <span class="op">&lt;-</span> <span class="fl">0.05</span></span>
+<span></span>
+<span><span class="co"># Identify major viral classes</span></span>
+<span><span class="va">viral_classes_major_tab</span> <span class="op">&lt;-</span> <span class="va">viral_classes</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_viral_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_viral</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_viral_max</span> <span class="op">&gt;=</span> <span class="va">major_threshold</span><span class="op">)</span></span>
+<span><span class="va">viral_classes_major_list</span> <span class="op">&lt;-</span> <span class="va">viral_classes_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span><span class="va">viral_classes_major</span> <span class="op">&lt;-</span> <span class="va">viral_classes</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">viral_classes_major_list</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">p_reads_viral</span><span class="op">)</span></span>
+<span><span class="va">viral_classes_minor</span> <span class="op">&lt;-</span> <span class="va">viral_classes_major</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_viral_major <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_viral</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Other"</span>, taxid<span class="op">=</span><span class="cn">NA</span>, p_reads_viral <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_viral_major</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">p_reads_viral</span><span class="op">)</span></span>
+<span><span class="va">viral_classes_display</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">viral_classes_major</span>, <span class="va">viral_classes_minor</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_viral</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name</span>, levels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">viral_classes_major_list</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>         p_reads_viral <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">pmax</a></span><span class="op">(</span><span class="va">p_reads_viral</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_viral</span>, classification<span class="op">=</span><span class="va">name</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">palette_viral</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fu">brewer.pal</span><span class="op">(</span><span class="fl">12</span>, <span class="st">"Set3"</span><span class="op">)</span>, <span class="fu">brewer.pal</span><span class="op">(</span><span class="fl">8</span>, <span class="st">"Dark2"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_classes</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">viral_classes_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Viral Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral class"</span><span class="op">)</span></span>
+<span>  </span>
+<span><span class="va">g_classes</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/viral-class-composition-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p><em>Papovaviricetes</em> are quite heterogeneous across samples, and frequently diverse within samples. <em>Alphapolyomavirus</em> and <em>Alphapapillomavirus</em> are the most abundant genera overall, but <em>Betapapillomavirus, Gammapapillomavirus, Mupapillomavirus</em> and others all have strong showings.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb21"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get samples</span></span>
+<span><span class="va">papova_taxid</span> <span class="op">&lt;-</span> <span class="fl">2732421</span></span>
+<span><span class="va">papova_threshold</span> <span class="op">&lt;-</span> <span class="fl">0.05</span></span>
+<span><span class="va">papova_samples</span> <span class="op">&lt;-</span> <span class="va">viral_classes</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">papova_taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_viral</span> <span class="op">&gt;</span> <span class="fl">0.05</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">unique</span></span>
+<span></span>
+<span><span class="co"># Get all taxa in class</span></span>
+<span><span class="va">papova_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">papova_taxid</span></span>
+<span><span class="va">papova_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">papova_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">papova_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="kw">while</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">papova_desc_taxids_new</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">papova_desc_taxids_old</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">papova_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">papova_desc_taxids_new</span></span>
+<span>  <span class="va">papova_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">papova_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">papova_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="co"># Get read counts</span></span>
+<span><span class="va">papova_counts</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral_cleaned</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">papova_desc_taxids_new</span>,</span>
+<span>         <span class="va">sample</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">papova_samples</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_papova <span class="op">=</span> <span class="va">n_reads_clade</span><span class="op">/</span><span class="va">n_reads_clade</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get genus composition</span></span>
+<span><span class="va">papova_genera</span> <span class="op">&lt;-</span> <span class="va">papova_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"G"</span><span class="op">)</span></span>
+<span><span class="va">papova_genera_major_tab</span> <span class="op">&lt;-</span> <span class="va">papova_genera</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_papova_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_papova</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_papova_max</span> <span class="op">&gt;=</span> <span class="va">papova_threshold</span><span class="op">)</span></span>
+<span><span class="va">papova_genera_major_list</span> <span class="op">&lt;-</span> <span class="va">papova_genera_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span><span class="va">papova_genera_major</span> <span class="op">&lt;-</span> <span class="va">papova_genera</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">papova_genera_major_list</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">p_reads_papova</span><span class="op">)</span></span>
+<span><span class="va">papova_genera_minor</span> <span class="op">&lt;-</span> <span class="va">papova_genera_major</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_papova_major <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_papova</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Other"</span>, taxid<span class="op">=</span><span class="cn">NA</span>, p_reads_papova <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_papova_major</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">p_reads_papova</span><span class="op">)</span></span>
+<span><span class="va">papova_genera_display</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">papova_genera_major</span>, <span class="va">papova_genera_minor</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_papova</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name</span>, levels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">papova_genera_major_list</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_papova</span>, classification<span class="op">=</span><span class="va">name</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_papova_genera</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">papova_genera_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Papovaviricetes Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.02</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral genus"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_papova_genera</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/papovaviricetes-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>Only a few samples showed at least 5% prevalence of <em>Herviviricetes</em>, but those that did were typically dominated by one or a small number of species that varied between samples. Of these, human alphaherpesvirus 1 appeared in the most samples, but several other species were prominent in at least one sample:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb22"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get samples</span></span>
+<span><span class="va">hervi_taxid</span> <span class="op">&lt;-</span> <span class="fl">2731363</span></span>
+<span><span class="va">hervi_threshold</span> <span class="op">&lt;-</span> <span class="fl">0.05</span></span>
+<span><span class="va">hervi_samples</span> <span class="op">&lt;-</span> <span class="va">viral_classes</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">hervi_taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_viral</span> <span class="op">&gt;</span> <span class="fl">0.05</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">unique</span></span>
+<span></span>
+<span><span class="co"># Get all taxa in class</span></span>
+<span><span class="va">hervi_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">hervi_taxid</span></span>
+<span><span class="va">hervi_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hervi_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hervi_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="kw">while</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">hervi_desc_taxids_new</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">hervi_desc_taxids_old</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">hervi_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">hervi_desc_taxids_new</span></span>
+<span>  <span class="va">hervi_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hervi_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hervi_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="co"># Get read counts</span></span>
+<span><span class="va">hervi_counts</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral_cleaned</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hervi_desc_taxids_new</span>,</span>
+<span>         <span class="va">sample</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hervi_samples</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_hervi <span class="op">=</span> <span class="va">n_reads_clade</span><span class="op">/</span><span class="va">n_reads_clade</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get genus composition</span></span>
+<span><span class="va">hervi_genera</span> <span class="op">&lt;-</span> <span class="va">hervi_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"S"</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_major_tab</span> <span class="op">&lt;-</span> <span class="va">hervi_genera</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_hervi_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_hervi</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hervi_max</span> <span class="op">&gt;=</span> <span class="va">hervi_threshold</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_major_list</span> <span class="op">&lt;-</span> <span class="va">hervi_genera_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_major</span> <span class="op">&lt;-</span> <span class="va">hervi_genera</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hervi_genera_major_list</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">p_reads_hervi</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_minor</span> <span class="op">&lt;-</span> <span class="va">hervi_genera_major</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_hervi_major <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_hervi</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Other"</span>, taxid<span class="op">=</span><span class="cn">NA</span>, p_reads_hervi <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_hervi_major</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">p_reads_hervi</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_display</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">hervi_genera_major</span>, <span class="va">hervi_genera_minor</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_hervi</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name</span>, levels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hervi_genera_major_list</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_hervi</span>, classification<span class="op">=</span><span class="va">name</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_hervi_genera</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">hervi_genera_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Herviviricetes Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral genus"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_hervi_genera</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/herviviricetes-1.png" class="img-fluid figure-img" width="864"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>Finally, for <em>Revtraviricetes</em>, most samples were dominated by porcine type-C oncovirus, while one was dominated by an avian retrovirus. The last showed significant levels of two murine viruses plus HIV. I’m suspicious of many of these.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb23"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get samples</span></span>
+<span><span class="va">revtra_taxid</span> <span class="op">&lt;-</span> <span class="fl">2732514</span></span>
+<span><span class="va">revtra_threshold</span> <span class="op">&lt;-</span> <span class="fl">0.05</span></span>
+<span><span class="va">revtra_samples</span> <span class="op">&lt;-</span> <span class="va">viral_classes</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">revtra_taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_viral</span> <span class="op">&gt;</span> <span class="fl">0.05</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">unique</span></span>
+<span></span>
+<span><span class="co"># Get all taxa in class</span></span>
+<span><span class="va">revtra_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">revtra_taxid</span></span>
+<span><span class="va">revtra_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">revtra_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">revtra_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="kw">while</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">revtra_desc_taxids_new</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">revtra_desc_taxids_old</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">revtra_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">revtra_desc_taxids_new</span></span>
+<span>  <span class="va">revtra_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">revtra_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">revtra_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="co"># Get read counts</span></span>
+<span><span class="va">revtra_counts</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral_cleaned</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">revtra_desc_taxids_new</span>,</span>
+<span>         <span class="va">sample</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">revtra_samples</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_revtra <span class="op">=</span> <span class="va">n_reads_clade</span><span class="op">/</span><span class="va">n_reads_clade</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get genus composition</span></span>
+<span><span class="va">revtra_genera</span> <span class="op">&lt;-</span> <span class="va">revtra_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"S"</span><span class="op">)</span></span>
+<span><span class="va">revtra_genera_major_tab</span> <span class="op">&lt;-</span> <span class="va">revtra_genera</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_revtra_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_revtra</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_revtra_max</span> <span class="op">&gt;=</span> <span class="va">revtra_threshold</span><span class="op">)</span></span>
+<span><span class="va">revtra_genera_major_list</span> <span class="op">&lt;-</span> <span class="va">revtra_genera_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span><span class="va">revtra_genera_major</span> <span class="op">&lt;-</span> <span class="va">revtra_genera</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">revtra_genera_major_list</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">p_reads_revtra</span><span class="op">)</span></span>
+<span><span class="va">revtra_genera_minor</span> <span class="op">&lt;-</span> <span class="va">revtra_genera_major</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_revtra_major <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_revtra</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Other"</span>, taxid<span class="op">=</span><span class="cn">NA</span>, p_reads_revtra <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_revtra_major</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">p_reads_revtra</span><span class="op">)</span></span>
+<span><span class="va">revtra_genera_display</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">revtra_genera_major</span>, <span class="va">revtra_genera_minor</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_revtra</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name</span>, levels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">revtra_genera_major_list</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_revtra</span>, classification<span class="op">=</span><span class="va">name</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_revtra_genera</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">revtra_genera_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% revtraviricetes Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral genus"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_revtra_genera</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/revtraviricetes-1.png" class="img-fluid figure-img" width="864"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="human-infecting-virus-reads-validation" class="level1"><h1>Human-infecting virus reads: validation</h1>
+<p>Next, I investigated the human-infecting virus read content of these unenriched samples. Using the same workflow I used for Prussin et al, I identified 24,278 read pairs as putatively human viral: 0.002% of reads surviving to that stage in the pipeline.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb24"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Import HV read data</span></span>
+<span><span class="va">hv_reads_filtered_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dirs</span>, <span class="st">"hv_hits_putative_filtered.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">hv_reads_filtered</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">hv_reads_filtered_paths</span>, <span class="va">read_tsv</span>,</span>
+<span>                            show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">bind_rows</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count reads</span></span>
+<span><span class="va">n_hv_filtered</span> <span class="op">&lt;-</span> <span class="va">hv_reads_filtered</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">seq_id</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">basic_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"ribo_initial"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>               <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">n_read_pairs</span><span class="op">)</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">rename</span><span class="op">(</span>n_putative <span class="op">=</span> <span class="va">n</span>, n_total <span class="op">=</span> <span class="va">n_read_pairs</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">n_putative</span><span class="op">/</span><span class="va">n_total</span>, pc_reads <span class="op">=</span> <span class="va">p_reads</span> <span class="op">*</span> <span class="fl">100</span><span class="op">)</span></span>
+<span><span class="va">n_hv_filtered_summ</span> <span class="op">&lt;-</span> <span class="va">n_hv_filtered</span> <span class="op">%&gt;%</span> <span class="va">ungroup</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_putative <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_putative</span><span class="op">)</span>, n_total <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_total</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">n_putative</span><span class="op">/</span><span class="va">n_total</span>, pc_reads <span class="op">=</span> <span class="va">p_reads</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb25"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Collapse multi-entry sequences</span></span>
+<span><span class="va">rmax</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">max</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="va">collapse</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span><span class="op">(</span><span class="va">x</span> <span class="op">==</span> <span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span>, <span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste</a></span><span class="op">(</span><span class="va">x</span>, collapse<span class="op">=</span><span class="st">"/"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">mrg</span> <span class="op">&lt;-</span> <span class="va">hv_reads_filtered</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>adj_score_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">pmax</a></span><span class="op">(</span><span class="va">adj_score_fwd</span>, <span class="va">adj_score_rev</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">adj_score_max</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">seq_id</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>sample <span class="op">=</span> <span class="fu">collapse</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span>,</span>
+<span>            genome_id <span class="op">=</span> <span class="fu">collapse</span><span class="op">(</span><span class="va">genome_id</span><span class="op">)</span>,</span>
+<span>            taxid_best <span class="op">=</span> <span class="va">taxid</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
+<span>            taxid <span class="op">=</span> <span class="fu">collapse</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>            best_alignment_score_fwd <span class="op">=</span> <span class="fu">rmax</span><span class="op">(</span><span class="va">best_alignment_score_fwd</span><span class="op">)</span>,</span>
+<span>            best_alignment_score_rev <span class="op">=</span> <span class="fu">rmax</span><span class="op">(</span><span class="va">best_alignment_score_rev</span><span class="op">)</span>,</span>
+<span>            query_len_fwd <span class="op">=</span> <span class="fu">rmax</span><span class="op">(</span><span class="va">query_len_fwd</span><span class="op">)</span>,</span>
+<span>            query_len_rev <span class="op">=</span> <span class="fu">rmax</span><span class="op">(</span><span class="va">query_len_rev</span><span class="op">)</span>,</span>
+<span>            query_seq_fwd <span class="op">=</span> <span class="va">query_seq_fwd</span><span class="op">[</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">query_seq_fwd</span><span class="op">)</span><span class="op">]</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
+<span>            query_seq_rev <span class="op">=</span> <span class="va">query_seq_rev</span><span class="op">[</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">query_seq_rev</span><span class="op">)</span><span class="op">]</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
+<span>            classified <span class="op">=</span> <span class="fu">rmax</span><span class="op">(</span><span class="va">classified</span><span class="op">)</span>,</span>
+<span>            assigned_name <span class="op">=</span> <span class="fu">collapse</span><span class="op">(</span><span class="va">assigned_name</span><span class="op">)</span>,</span>
+<span>            assigned_taxid_best <span class="op">=</span> <span class="va">assigned_taxid</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
+<span>            assigned_taxid <span class="op">=</span> <span class="fu">collapse</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">assigned_taxid</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>            assigned_hv <span class="op">=</span> <span class="fu">rmax</span><span class="op">(</span><span class="va">assigned_hv</span><span class="op">)</span>,</span>
+<span>            hit_hv <span class="op">=</span> <span class="fu">rmax</span><span class="op">(</span><span class="va">hit_hv</span><span class="op">)</span>,</span>
+<span>            encoded_hits <span class="op">=</span> <span class="fu">collapse</span><span class="op">(</span><span class="va">encoded_hits</span><span class="op">)</span>,</span>
+<span>            adj_score_fwd <span class="op">=</span> <span class="fu">rmax</span><span class="op">(</span><span class="va">adj_score_fwd</span><span class="op">)</span>,</span>
+<span>            adj_score_rev <span class="op">=</span> <span class="fu">rmax</span><span class="op">(</span><span class="va">adj_score_rev</span><span class="op">)</span></span>
+<span>            <span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>kraken_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">assigned_hv</span>, <span class="st">"Kraken2 HV\nassignment"</span>,</span>
+<span>                               <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">hit_hv</span>, <span class="st">"Kraken2 HV\nhit"</span>,</span>
+<span>                                      <span class="st">"No hit or\nassignment"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>adj_score_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">pmax</a></span><span class="op">(</span><span class="va">adj_score_fwd</span>, <span class="va">adj_score_rev</span><span class="op">)</span>,</span>
+<span>         highscore <span class="op">=</span> <span class="va">adj_score_max</span> <span class="op">&gt;=</span> <span class="fl">20</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_hist_0</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">mrg</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_max</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_histogram</span><span class="op">(</span>binwidth<span class="op">=</span><span class="fl">5</span>,boundary<span class="op">=</span><span class="fl">0</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">20</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">kraken_label</span>, labeller <span class="op">=</span> <span class="fu">labeller</span><span class="op">(</span>kit <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span><span class="fl">20</span><span class="op">)</span><span class="op">)</span>, scales <span class="op">=</span> <span class="st">"free_y"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Maximum adjusted alignment score"</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Read pairs"</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="va">theme_base</span> </span>
+<span><span class="va">g_hist_0</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-hv-scores-1.png" class="img-fluid figure-img" width="768"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>As previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I’ve used for previous datasets.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb26"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">mrg_fasta</span> <span class="op">&lt;-</span>  <span class="va">mrg</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>seq_head <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">"&gt;"</span>, <span class="va">seq_id</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="va">ungroup</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span>header1<span class="op">=</span><span class="va">seq_head</span>, seq1<span class="op">=</span><span class="va">query_seq_fwd</span>, </span>
+<span>         header2<span class="op">=</span><span class="va">seq_head</span>, seq2<span class="op">=</span><span class="va">query_seq_rev</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>header1<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">header1</span>, <span class="st">"_1"</span><span class="op">)</span>, header2<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">header2</span>, <span class="st">"_2"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">mrg_fasta_sep</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="fu">select</span><span class="op">(</span><span class="va">mrg_fasta</span>, header<span class="op">=</span><span class="va">header1</span>, seq<span class="op">=</span><span class="va">seq1</span><span class="op">)</span>,</span>
+<span>                           <span class="fu">select</span><span class="op">(</span><span class="va">mrg_fasta</span>, header<span class="op">=</span><span class="va">header2</span>, seq<span class="op">=</span><span class="va">seq2</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">seq</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">mrg_fasta_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/do.call.html">do.call</a></span><span class="op">(</span><span class="va">paste</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">mrg_fasta_sep</span>, sep<span class="op">=</span><span class="st">"\n"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste</a></span><span class="op">(</span>collapse<span class="op">=</span><span class="st">"\n"</span><span class="op">)</span></span>
+<span><span class="va">blast_dir</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir_base</span>, <span class="st">"blast"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/files2.html">dir.create</a></span><span class="op">(</span><span class="va">blast_dir</span>, showWarnings <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/write.html">write</a></span><span class="op">(</span><span class="va">mrg_fasta_out</span>, <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">blast_dir</span>, <span class="st">"putative-viral.fasta"</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb27"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Import BLAST results</span></span>
+<span><span class="co"># blast_results_path &lt;- file.path(data_dir_base, "blast/putative-viral.blast.gz")</span></span>
+<span><span class="co"># blast_cols &lt;- c("qseqid", "sseqid", "sgi", "staxid", "qlen", "evalue", "bitscore", "qcovs", "length", "pident", "mismatch", "gapopen", "sstrand", "qstart", "qend", "sstart", "send")</span></span>
+<span><span class="co"># blast_results &lt;- read_tsv(blast_results_path, show_col_types = FALSE,</span></span>
+<span><span class="co">#                           col_names = blast_cols, col_types = cols(.default="c"))</span></span>
+<span><span class="va">blast_results_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir_base</span>, <span class="st">"blast/putative-viral-best.blast.gz"</span><span class="op">)</span></span>
+<span><span class="va">blast_results</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">blast_results_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Filter for best hit for each query/subject combination</span></span>
+<span><span class="va">blast_results_best</span> <span class="op">&lt;-</span> <span class="va">blast_results</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">qseqid</span>, <span class="va">staxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">bitscore</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">length</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">length</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span></span>
+<span><span class="fu">write_tsv</span><span class="op">(</span><span class="va">blast_results_best</span>, <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir_base</span>, <span class="st">"blast/putative-viral-best.blast.gz"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Rank hits for each query and filter for high-ranking hits</span></span>
+<span><span class="va">blast_results_ranked</span> <span class="op">&lt;-</span> <span class="va">blast_results_best</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">qseqid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>rank <span class="op">=</span> <span class="fu">dense_rank</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">blast_results_highrank</span> <span class="op">&lt;-</span> <span class="va">blast_results_ranked</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">&lt;=</span> <span class="fl">5</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>read_pair <span class="op">=</span> <span class="fu">str_split</span><span class="op">(</span><span class="va">qseqid</span>, <span class="st">"_"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">nth</span>, n<span class="op">=</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span>, </span>
+<span>         seq_id <span class="op">=</span> <span class="fu">str_split</span><span class="op">(</span><span class="va">qseqid</span>, <span class="st">"_"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">nth</span>, n<span class="op">=</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>bitscore <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Summarize by read pair and taxid</span></span>
+<span><span class="va">blast_results_paired</span> <span class="op">&lt;-</span> <span class="va">blast_results_highrank</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">seq_id</span>, <span class="va">staxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>bitscore_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span>, bitscore_min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span>,</span>
+<span>            n_reads <span class="op">=</span> <span class="fu">n</span><span class="op">(</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Add viral status</span></span>
+<span><span class="va">blast_results_viral</span> <span class="op">&lt;-</span> <span class="fu">mutate</span><span class="op">(</span><span class="va">blast_results_paired</span>, viral <span class="op">=</span> <span class="va">staxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>viral_full <span class="op">=</span> <span class="va">viral</span> <span class="op">&amp;</span> <span class="va">n_reads</span> <span class="op">==</span> <span class="fl">2</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Compare to Kraken &amp; Bowtie assignments</span></span>
+<span><span class="va">match_taxid</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">taxid_1</span>, <span class="va">taxid_2</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">p1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/mapply.html">mapply</a></span><span class="op">(</span><span class="va">grepl</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">"/"</span>, <span class="va">taxid_1</span>, <span class="st">"$"</span><span class="op">)</span>, <span class="va">taxid_2</span><span class="op">)</span></span>
+<span>  <span class="va">p2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/mapply.html">mapply</a></span><span class="op">(</span><span class="va">grepl</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">"^"</span>, <span class="va">taxid_1</span>, <span class="st">"/"</span><span class="op">)</span>, <span class="va">taxid_2</span><span class="op">)</span></span>
+<span>  <span class="va">p3</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/mapply.html">mapply</a></span><span class="op">(</span><span class="va">grepl</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">"^"</span>, <span class="va">taxid_1</span>, <span class="st">"$"</span><span class="op">)</span>, <span class="va">taxid_2</span><span class="op">)</span></span>
+<span>  <span class="va">out</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/setNames.html">setNames</a></span><span class="op">(</span><span class="va">p1</span><span class="op">|</span><span class="va">p2</span><span class="op">|</span><span class="va">p3</span>, <span class="cn">NULL</span><span class="op">)</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">out</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">mrg_assign</span> <span class="op">&lt;-</span> <span class="va">mrg</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">seq_id</span>, <span class="va">taxid</span>, <span class="va">assigned_taxid</span>, <span class="va">adj_score_max</span><span class="op">)</span></span>
+<span><span class="va">blast_results_assign</span> <span class="op">&lt;-</span> <span class="fu">inner_join</span><span class="op">(</span><span class="va">blast_results_viral</span>, <span class="va">mrg_assign</span>, by<span class="op">=</span><span class="st">"seq_id"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>taxid_match_bowtie <span class="op">=</span> <span class="fu">match_taxid</span><span class="op">(</span><span class="va">staxid</span>, <span class="va">taxid</span><span class="op">)</span>,</span>
+<span>           taxid_match_kraken <span class="op">=</span> <span class="fu">match_taxid</span><span class="op">(</span><span class="va">staxid</span>, <span class="va">assigned_taxid</span><span class="op">)</span>,</span>
+<span>           taxid_match_any <span class="op">=</span> <span class="va">taxid_match_bowtie</span> <span class="op">|</span> <span class="va">taxid_match_kraken</span><span class="op">)</span></span>
+<span><span class="va">blast_results_out</span> <span class="op">&lt;-</span> <span class="va">blast_results_assign</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">seq_id</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>viral_status <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span><span class="op">(</span><span class="va">viral_full</span><span class="op">)</span>, <span class="fl">2</span>,</span>
+<span>                                  <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span><span class="op">(</span><span class="va">taxid_match_any</span><span class="op">)</span>, <span class="fl">2</span>,</span>
+<span>                                             <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span><span class="op">(</span><span class="va">viral</span><span class="op">)</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>            .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb28"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Merge BLAST results with unenriched read data</span></span>
+<span><span class="va">mrg_blast</span> <span class="op">&lt;-</span> <span class="fu">full_join</span><span class="op">(</span><span class="va">mrg</span>, <span class="va">blast_results_out</span>, by<span class="op">=</span><span class="st">"seq_id"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>viral_status <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">viral_status</span>, <span class="fl">0</span><span class="op">)</span>,</span>
+<span>         viral_status_out <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">viral_status</span> <span class="op">==</span> <span class="fl">0</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot RNA</span></span>
+<span><span class="va">g_hist_1</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">mrg_blast</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_max</span>, fill<span class="op">=</span><span class="va">viral_status_out</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_histogram</span><span class="op">(</span>binwidth<span class="op">=</span><span class="fl">5</span>,boundary<span class="op">=</span><span class="fl">0</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">20</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">kraken_label</span>, labeller <span class="op">=</span> <span class="fu">labeller</span><span class="op">(</span>kit <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span><span class="fl">20</span><span class="op">)</span><span class="op">)</span>, scales <span class="op">=</span> <span class="st">"free_y"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Maximum adjusted alignment score"</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Read pairs"</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_fill_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Set1"</span>, name <span class="op">=</span> <span class="st">"Viral status"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span><span class="va">g_hist_1</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-blast-results-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>For a disjunctive score threshold of 20, the workflow achieves a measured F1 score of 98.0%.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb29"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">test_sens_spec</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">tab</span>, <span class="va">score_threshold</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">tab_retained</span> <span class="op">&lt;-</span> <span class="va">tab</span> <span class="op">%&gt;%</span> </span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>retain_score <span class="op">=</span> <span class="op">(</span><span class="va">adj_score_fwd</span> <span class="op">&gt;</span> <span class="va">score_threshold</span> <span class="op">|</span> <span class="va">adj_score_rev</span> <span class="op">&gt;</span> <span class="va">score_threshold</span><span class="op">)</span>,</span>
+<span>           retain <span class="op">=</span> <span class="va">assigned_hv</span> <span class="op">|</span> <span class="va">hit_hv</span> <span class="op">|</span> <span class="va">retain_score</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span></span>
+<span>  <span class="va">pos_tru</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">==</span> <span class="st">"TRUE"</span>, <span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">pos_fls</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">!=</span> <span class="st">"TRUE"</span>, <span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">neg_tru</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">!=</span> <span class="st">"TRUE"</span>, <span class="op">!</span><span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">neg_fls</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">==</span> <span class="st">"TRUE"</span>, <span class="op">!</span><span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">sensitivity</span> <span class="op">&lt;-</span> <span class="va">pos_tru</span> <span class="op">/</span> <span class="op">(</span><span class="va">pos_tru</span> <span class="op">+</span> <span class="va">neg_fls</span><span class="op">)</span></span>
+<span>  <span class="va">specificity</span> <span class="op">&lt;-</span> <span class="va">neg_tru</span> <span class="op">/</span> <span class="op">(</span><span class="va">neg_tru</span> <span class="op">+</span> <span class="va">pos_fls</span><span class="op">)</span></span>
+<span>  <span class="va">precision</span>   <span class="op">&lt;-</span> <span class="va">pos_tru</span> <span class="op">/</span> <span class="op">(</span><span class="va">pos_tru</span> <span class="op">+</span> <span class="va">pos_fls</span><span class="op">)</span></span>
+<span>  <span class="va">f1</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> <span class="va">precision</span> <span class="op">*</span> <span class="va">sensitivity</span> <span class="op">/</span> <span class="op">(</span><span class="va">precision</span> <span class="op">+</span> <span class="va">sensitivity</span><span class="op">)</span></span>
+<span>  <span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="op">(</span>threshold<span class="op">=</span><span class="va">score_threshold</span>, sensitivity<span class="op">=</span><span class="va">sensitivity</span>, </span>
+<span>                specificity<span class="op">=</span><span class="va">specificity</span>, precision<span class="op">=</span><span class="va">precision</span>, f1<span class="op">=</span><span class="va">f1</span><span class="op">)</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">out</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">range_f1</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">intab</span>, <span class="va">inrange</span><span class="op">=</span><span class="fl">15</span><span class="op">:</span><span class="fl">45</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">tss</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">test_sens_spec</span>, tab<span class="op">=</span><span class="va">intab</span><span class="op">)</span></span>
+<span>  <span class="va">stats</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">inrange</span>, <span class="va">tss</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">pivot_longer</span><span class="op">(</span><span class="op">!</span><span class="va">threshold</span>, names_to<span class="op">=</span><span class="st">"metric"</span>, values_to<span class="op">=</span><span class="st">"value"</span><span class="op">)</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">stats</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">stats_0</span> <span class="op">&lt;-</span> <span class="fu">range_f1</span><span class="op">(</span><span class="va">mrg_blast</span><span class="op">)</span></span>
+<span><span class="va">g_stats_0</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">stats_0</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">threshold</span>, y<span class="op">=</span><span class="va">value</span>, color<span class="op">=</span><span class="va">metric</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">20</span>, color <span class="op">=</span> <span class="st">"red"</span>, linetype <span class="op">=</span> <span class="st">"dashed"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Value"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Adjusted Score Threshold"</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_brewer</span><span class="op">(</span>palette<span class="op">=</span><span class="st">"Dark2"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span><span class="va">g_stats_0</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-f1-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb30"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">stats_0</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">threshold</span> <span class="op">==</span> <span class="fl">20</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">select</span><span class="op">(</span>Threshold<span class="op">=</span><span class="va">threshold</span>, Metric<span class="op">=</span><span class="va">metric</span>, Value<span class="op">=</span><span class="va">value</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div data-pagedtable="false">
+  <script data-pagedtable-source="" type="application/json">
+{"columns":[{"label":["Threshold"],"name":[1],"type":["int"],"align":["right"]},{"label":["Metric"],"name":[2],"type":["chr"],"align":["left"]},{"label":["Value"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"20","2":"sensitivity","3":"0.9681598"},{"1":"20","2":"specificity","3":"0.6560636"},{"1":"20","2":"precision","3":"0.9925402"},{"1":"20","2":"f1","3":"0.9801984"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+  </script>
+</div>
+</div>
+</div>
+<p>Looking into the composition of different read groups, the notable observation for me is the high prevalence of Pigeon torque teno virus among high-scoring false positives, with 77 such read pairs. BLAST maps these not to viruses but to their most common hosts, i.e.&nbsp;assorted species of pigeon. That said, the number of false positive PTTV reads is substantially exceeded by the number of true-positive PTTV reads (1883), which do map to appropriate viruses according to BLAST, so the presence of a comparatively small number of false positives seems unlikely to cause too much distortion.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb31"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">major_threshold</span> <span class="op">&lt;-</span> <span class="fl">0.04</span></span>
+<span></span>
+<span><span class="co"># Add missing viral taxa</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">211787</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Human papillomavirus type 92"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">509154</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Porcine endogenous retrovirus C"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">493803</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Merkel cell polyomavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">427343</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Human papillomavirus 107"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">194958</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Porcine endogenous retrovirus A"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">340907</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Papiine alphaherpesvirus 2"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">194959</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Porcine endogenous retrovirus B"</span></span>
+<span></span>
+<span></span>
+<span><span class="co"># Prepare data</span></span>
+<span><span class="va">fp</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">highscore</span>, <span class="va">taxid_best</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">highscore</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p<span class="op">=</span><span class="va">n</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">rename</span><span class="op">(</span>taxid <span class="op">=</span> <span class="va">taxid_best</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">viral_taxa</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">fp_major_tab</span> <span class="op">&lt;-</span> <span class="va">fp</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p</span> <span class="op">&gt;</span> <span class="va">major_threshold</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">fp_major_list</span> <span class="op">&lt;-</span> <span class="va">fp_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sort</span> <span class="op">%&gt;%</span> <span class="va">unique</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">.</span>, <span class="st">"Other"</span><span class="op">)</span></span>
+<span><span class="va">fp_major</span> <span class="op">&lt;-</span> <span class="va">fp</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>major <span class="op">=</span> <span class="va">p</span> <span class="op">&gt;</span> <span class="va">major_threshold</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">major</span>, <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">highscore</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n</span><span class="op">)</span>, p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span>  <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, levels <span class="op">=</span> <span class="va">fp_major_list</span><span class="op">)</span>,</span>
+<span>         score_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">highscore</span>, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span><span class="op">)</span>,</span>
+<span>         status_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="st">"True positive"</span>, <span class="st">"False positive"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_fp</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">fp_major</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">score_display</span>, y<span class="op">=</span><span class="va">p</span>, fill<span class="op">=</span><span class="va">name_display</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position<span class="op">=</span><span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_discrete</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"True positive?"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"% reads"</span>, limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values <span class="op">=</span> <span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral\ntaxon"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">.</span><span class="op">~</span><span class="va">status_display</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_fp</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/fp-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb32"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Configure</span></span>
+<span><span class="va">ref_taxid_ptt</span> <span class="op">&lt;-</span> <span class="fl">2233536</span></span>
+<span><span class="va">p_threshold</span> <span class="op">&lt;-</span> <span class="fl">0.3</span></span>
+<span></span>
+<span><span class="co"># Get taxon names</span></span>
+<span><span class="va">tax_names_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir_base</span>, <span class="st">"taxid-names.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">tax_names</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">tax_names_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Add missing names</span></span>
+<span><span class="va">tax_names_new</span> <span class="op">&lt;-</span> <span class="fu">tribble</span><span class="op">(</span><span class="op">~</span><span class="va">staxid</span>, <span class="op">~</span><span class="va">name</span>,</span>
+<span>                         <span class="fl">3050295</span>, <span class="st">"Cytomegalovirus humanbeta5"</span>,</span>
+<span>                         <span class="fl">459231</span>, <span class="st">"FLAG-tagging vector pFLAG97-TSR"</span>,</span>
+<span>                         <span class="fl">3082113</span>, <span class="st">"Rangifer tarandus platyrhynchus"</span>,</span>
+<span>                         <span class="fl">3119969</span>, <span class="st">"Bubalus kerabau"</span>,</span>
+<span>                         <span class="fl">177155</span>, <span class="st">"Streptopelia turtur"</span>,</span>
+<span>                         <span class="fl">187126</span>, <span class="st">"Nesoenas mayeri"</span></span>
+<span>                         <span class="op">)</span></span>
+<span><span class="va">tax_names</span> <span class="op">&lt;-</span> <span class="va">tax_names_new</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="op">!</span> <span class="va">staxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">tax_names</span><span class="op">$</span><span class="va">staxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">bind_rows</span><span class="op">(</span><span class="va">tax_names</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="va">staxid</span><span class="op">)</span></span>
+<span><span class="va">ref_name_ptt</span> <span class="op">&lt;-</span> <span class="va">tax_names</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">staxid</span> <span class="op">==</span> <span class="va">ref_taxid_ptt</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get major matches</span></span>
+<span><span class="va">mrg_staxid</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid_best</span> <span class="op">==</span> <span class="va">ref_taxid_ptt</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">group_by</span><span class="op">(</span><span class="va">highscore</span>, <span class="va">viral_status_out</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>n_seq <span class="op">=</span> <span class="fu">n</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">fp_staxid</span> <span class="op">&lt;-</span> <span class="va">mrg_staxid</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">blast_results_paired</span>, by<span class="op">=</span><span class="st">"seq_id"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>staxid <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html">as.integer</a></span><span class="op">(</span><span class="va">staxid</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">tax_names</span>, by<span class="op">=</span><span class="st">"staxid"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>sname<span class="op">=</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">tax_names</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>taxid_best<span class="op">=</span><span class="va">staxid</span><span class="op">)</span>, by<span class="op">=</span><span class="st">"taxid_best"</span><span class="op">)</span></span>
+<span><span class="va">fp_staxid_count</span> <span class="op">&lt;-</span> <span class="va">fp_staxid</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">highscore</span>, </span>
+<span>           <span class="va">taxid_best</span>, <span class="va">name</span>, <span class="va">staxid</span>, <span class="va">sname</span>, <span class="va">n_seq</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="va">count</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">highscore</span>, <span class="va">taxid_best</span>, <span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p<span class="op">=</span><span class="va">n</span><span class="op">/</span><span class="va">n_seq</span><span class="op">)</span></span>
+<span><span class="va">fp_staxid_count_major</span> <span class="op">&lt;-</span> <span class="va">fp_staxid_count</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">n</span><span class="op">&gt;</span><span class="fl">1</span>, <span class="va">p</span><span class="op">&gt;</span><span class="va">p_threshold</span>, <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">staxid</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>score_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">highscore</span>, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span><span class="op">)</span>,</span>
+<span>         status_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">viral_status_out</span>, </span>
+<span>                                 <span class="st">"True positive"</span>, <span class="st">"False positive"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">fp_staxid_count_major</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">p</span>, y<span class="op">=</span><span class="va">sname</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">status_display</span><span class="op">~</span><span class="va">score_display</span>, scales<span class="op">=</span><span class="st">"free"</span>,</span>
+<span>             labeller <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span>multi_line <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% mapped reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">ref_name_ptt</span>, <span class="st">" (taxid "</span>, <span class="va">ref_taxid_ptt</span>, <span class="st">")"</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span></span>
+<span>    axis.title.y <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span>,</span>
+<span>    plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">1.4</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span>, face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/ptt-blast-hits-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="human-infecting-viruses-overall-relative-abundance" class="level1"><h1>Human-infecting viruses: overall relative abundance</h1>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb33"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get raw read counts</span></span>
+<span><span class="va">read_counts_raw</span> <span class="op">&lt;-</span> <span class="va">basic_stats_raw</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, n_reads_raw <span class="op">=</span> <span class="va">n_read_pairs</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get HV read counts</span></span>
+<span><span class="va">mrg_hv</span> <span class="op">&lt;-</span> <span class="va">mrg</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>hv_status <span class="op">=</span> <span class="va">assigned_hv</span> <span class="op">|</span> <span class="va">hit_hv</span> <span class="op">|</span> <span class="va">highscore</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>taxid_all <span class="op">=</span> <span class="va">taxid</span>, taxid <span class="op">=</span> <span class="va">taxid_best</span><span class="op">)</span></span>
+<span><span class="va">read_counts_hv</span> <span class="op">&lt;-</span> <span class="va">mrg_hv</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">hv_status</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">count</span><span class="op">(</span>name<span class="op">=</span><span class="st">"n_reads_hv"</span><span class="op">)</span></span>
+<span><span class="va">read_counts</span> <span class="op">&lt;-</span> <span class="va">read_counts_raw</span> <span class="op">%&gt;%</span> <span class="fu">left_join</span><span class="op">(</span><span class="va">read_counts_hv</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>n_reads_hv <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">n_reads_hv</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Aggregate</span></span>
+<span><span class="va">read_counts_city</span> <span class="op">&lt;-</span> <span class="va">read_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_raw <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_raw</span><span class="op">)</span>,</span>
+<span>            n_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>sample<span class="op">=</span> <span class="st">"All samples"</span>, date_alias <span class="op">=</span> <span class="st">"All dates"</span><span class="op">)</span></span>
+<span><span class="va">read_counts_total</span> <span class="op">&lt;-</span> <span class="va">read_counts_city</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_raw <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_raw</span><span class="op">)</span>,</span>
+<span>            n_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>city <span class="op">=</span> <span class="st">"All cities"</span><span class="op">)</span></span>
+<span><span class="va">read_counts_agg</span> <span class="op">&lt;-</span> <span class="va">read_counts_city</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">bind_rows</span><span class="op">(</span><span class="va">read_counts_total</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_hv <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="va">n_reads_raw</span>,</span>
+<span>         city <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">city</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<p>Applying a disjunctive cutoff at S=20 identifies 23,191 read pairs as human-viral. This gives an overall relative HV abundance of <span class="math inline">\(1.73 \times 10^{-5}\)</span>.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb34"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Visualize</span></span>
+<span><span class="va">g_phv_agg</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">read_counts_agg</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">city</span>, color<span class="op">=</span><span class="va">city</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_log10</span><span class="op">(</span><span class="st">"Relative abundance of human virus reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_discrete</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Collection Date"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co">#facet_grid(.~sample_type, scales = "free", space = "free_x") +</span></span>
+<span>  <span class="fu">scale_color_city</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="va">theme_rotate</span></span>
+<span><span class="va">g_phv_agg</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/plot-hv-ra-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>This is lower than for DNA reads from other air-sampling datasets I’ve analyzed, but not drastically so:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb35"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Collate past RA values</span></span>
+<span><span class="va">ra_past</span> <span class="op">&lt;-</span> <span class="fu">tribble</span><span class="op">(</span><span class="op">~</span><span class="va">dataset</span>, <span class="op">~</span><span class="va">ra</span>, <span class="op">~</span><span class="va">na_type</span>, <span class="op">~</span><span class="va">panel_enriched</span>,</span>
+<span>                   <span class="st">"Brumfield"</span>, <span class="fl">5e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Brumfield"</span>, <span class="fl">3.66e-7</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Spurbeck"</span>, <span class="fl">5.44e-6</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Yang"</span>, <span class="fl">3.62e-4</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Rothman (unenriched)"</span>, <span class="fl">1.87e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Rothman (panel-enriched)"</span>, <span class="fl">3.3e-5</span>, <span class="st">"RNA"</span>, <span class="cn">TRUE</span>,</span>
+<span>                   <span class="st">"Crits-Christoph (unenriched)"</span>, <span class="fl">1.37e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Crits-Christoph (panel-enriched)"</span>, <span class="fl">1.26e-2</span>, <span class="st">"RNA"</span>, <span class="cn">TRUE</span>,</span>
+<span>                   <span class="st">"Prussin (non-control)"</span>, <span class="fl">1.63e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Prussin (non-control)"</span>, <span class="fl">4.16e-5</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Rosario (non-control)"</span>, <span class="fl">1.21e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Rosario (non-control)"</span>, <span class="fl">1.50e-4</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Collate new RA values</span></span>
+<span><span class="va">ra_new</span> <span class="op">&lt;-</span> <span class="fu">tribble</span><span class="op">(</span><span class="op">~</span><span class="va">dataset</span>, <span class="op">~</span><span class="va">ra</span>, <span class="op">~</span><span class="va">na_type</span>, <span class="op">~</span><span class="va">panel_enriched</span>,</span>
+<span>                  <span class="st">"Leung"</span>, <span class="fl">1.73e-5</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">scale_color_na</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_color_brewer</span>, palette<span class="op">=</span><span class="st">"Set1"</span>,</span>
+<span>                                 name<span class="op">=</span><span class="st">"Nucleic acid type"</span><span class="op">)</span></span>
+<span><span class="va">ra_comp</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">ra_past</span>, <span class="va">ra_new</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>dataset <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">dataset</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_ra_comp</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">ra_comp</span>, <span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">dataset</span>, x<span class="op">=</span><span class="va">ra</span>, color<span class="op">=</span><span class="va">na_type</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_log10</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Relative abundance of human virus reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>axis.title.y <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_ra_comp</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/ra-hv-past-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="human-infecting-viruses-taxonomy-and-composition" class="level1"><h1>Human-infecting viruses: taxonomy and composition</h1>
+<p>At the family level, most samples across all cities are dominated by <em>Papillomaviridae</em>, <em>Herpesviridae</em>, <em>Anelloviridae</em>, <em>Polyomaviridae, and to a lesser extent</em> <em>Poxviridae</em>:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb36"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get viral taxon names for putative HV reads</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">249588</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Mamastrovirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">194960</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Kobuvirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">688449</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Salivirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">585893</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Picobirnaviridae"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333922</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334207</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 3"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">369960</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Porcine type-C oncovirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333924</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 2"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">687329</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Anelloviridae"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">325455</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Gammapapillomavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333750</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">694002</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betacoronavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334202</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Mupapillomavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">197911</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphainfluenzavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">186938</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Respirovirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333926</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Gammapapillomavirus 1"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">337051</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 1"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">337043</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus 4"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">694003</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betacoronavirus 1"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334204</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Mupapillomavirus 2"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334208</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333928</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Gammapapillomavirus 2"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">337039</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus 2"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333929</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Gammapapillomavirus 3"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">337042</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus 7"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334203</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Mupapillomavirus 1"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333757</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus 8"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">337050</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus 6"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333767</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus 3"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333754</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus 10"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">687363</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Torque teno virus 24"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">687342</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Torque teno virus 3"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">687359</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Torque teno virus 20"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">194441</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Primate T-lymphotropic virus 2"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334209</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 5"</span></span>
+<span></span>
+<span></span>
+<span><span class="va">mrg_hv_named</span> <span class="op">&lt;-</span> <span class="va">mrg_hv</span> <span class="op">%&gt;%</span> <span class="fu">left_join</span><span class="op">(</span><span class="va">viral_taxa</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Discover viral species &amp; genera for HV reads</span></span>
+<span><span class="va">raise_rank</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">read_db</span>, <span class="va">taxid_db</span>, <span class="va">out_rank</span> <span class="op">=</span> <span class="st">"species"</span>, <span class="va">verbose</span> <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="co"># Get higher ranks than search rank</span></span>
+<span>  <span class="va">ranks</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"subspecies"</span>, <span class="st">"species"</span>, <span class="st">"subgenus"</span>, <span class="st">"genus"</span>, <span class="st">"subfamily"</span>, <span class="st">"family"</span>, <span class="st">"suborder"</span>, <span class="st">"order"</span>, <span class="st">"class"</span>, <span class="st">"subphylum"</span>, <span class="st">"phylum"</span>, <span class="st">"kingdom"</span>, <span class="st">"superkingdom"</span><span class="op">)</span></span>
+<span>  <span class="va">rank_match</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/which.min.html">which.max</a></span><span class="op">(</span><span class="va">ranks</span> <span class="op">==</span> <span class="va">out_rank</span><span class="op">)</span></span>
+<span>  <span class="va">high_ranks</span> <span class="op">&lt;-</span> <span class="va">ranks</span><span class="op">[</span><span class="va">rank_match</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">ranks</span><span class="op">)</span><span class="op">]</span></span>
+<span>  <span class="co"># Merge read DB and taxid DB</span></span>
+<span>  <span class="va">reads</span> <span class="op">&lt;-</span> <span class="va">read_db</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="op">-</span><span class="va">parent_taxid</span>, <span class="op">-</span><span class="va">rank</span>, <span class="op">-</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">left_join</span><span class="op">(</span><span class="va">taxid_db</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span>  <span class="co"># Extract sequences that are already at appropriate rank</span></span>
+<span>  <span class="va">reads_rank</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">reads</span>, <span class="va">rank</span> <span class="op">==</span> <span class="va">out_rank</span><span class="op">)</span></span>
+<span>  <span class="co"># Drop sequences at a higher rank and return unclassified sequences</span></span>
+<span>  <span class="va">reads_norank</span> <span class="op">&lt;-</span> <span class="va">reads</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">!=</span> <span class="va">out_rank</span>, <span class="op">!</span><span class="va">rank</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">high_ranks</span>, <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="kw">while</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html">nrow</a></span><span class="op">(</span><span class="va">reads_norank</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fl">0</span><span class="op">)</span><span class="op">{</span> <span class="co"># As long as there are unclassified sequences...</span></span>
+<span>    <span class="co"># Promote read taxids and re-merge with taxid DB, then re-classify and filter</span></span>
+<span>    <span class="va">reads_remaining</span> <span class="op">&lt;-</span> <span class="va">reads_norank</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>taxid <span class="op">=</span> <span class="va">parent_taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>      <span class="fu">select</span><span class="op">(</span><span class="op">-</span><span class="va">parent_taxid</span>, <span class="op">-</span><span class="va">rank</span>, <span class="op">-</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>      <span class="fu">left_join</span><span class="op">(</span><span class="va">taxid_db</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span>    <span class="va">reads_rank</span> <span class="op">&lt;-</span> <span class="va">reads_remaining</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="va">out_rank</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>      <span class="fu">bind_rows</span><span class="op">(</span><span class="va">reads_rank</span><span class="op">)</span></span>
+<span>    <span class="va">reads_norank</span> <span class="op">&lt;-</span> <span class="va">reads_remaining</span> <span class="op">%&gt;%</span></span>
+<span>      <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">!=</span> <span class="va">out_rank</span>, <span class="op">!</span><span class="va">rank</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">high_ranks</span>, <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="op">}</span></span>
+<span>  <span class="co"># Finally, extract and append reads that were excluded during the process</span></span>
+<span>  <span class="va">reads_dropped</span> <span class="op">&lt;-</span> <span class="va">reads</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="op">!</span><span class="va">seq_id</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">reads_rank</span><span class="op">$</span><span class="va">seq_id</span><span class="op">)</span></span>
+<span>  <span class="va">reads_out</span> <span class="op">&lt;-</span> <span class="va">reads_rank</span> <span class="op">%&gt;%</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">reads_dropped</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">select</span><span class="op">(</span><span class="op">-</span><span class="va">parent_taxid</span>, <span class="op">-</span><span class="va">rank</span>, <span class="op">-</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">left_join</span><span class="op">(</span><span class="va">taxid_db</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">reads_out</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">hv_reads_species</span> <span class="op">&lt;-</span> <span class="fu">raise_rank</span><span class="op">(</span><span class="va">mrg_hv_named</span>, <span class="va">viral_taxa</span>, <span class="st">"species"</span><span class="op">)</span></span>
+<span><span class="va">hv_reads_genus</span> <span class="op">&lt;-</span> <span class="fu">raise_rank</span><span class="op">(</span><span class="va">mrg_hv_named</span>, <span class="va">viral_taxa</span>, <span class="st">"genus"</span><span class="op">)</span></span>
+<span><span class="va">hv_reads_family</span> <span class="op">&lt;-</span> <span class="fu">raise_rank</span><span class="op">(</span><span class="va">mrg_hv_named</span>, <span class="va">viral_taxa</span>, <span class="st">"family"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb37"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">threshold_major_family</span> <span class="op">&lt;-</span> <span class="fl">0.05</span></span>
+<span></span>
+<span><span class="co"># Count reads for each human-viral family</span></span>
+<span><span class="va">hv_family_counts</span> <span class="op">&lt;-</span> <span class="va">hv_reads_family</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">count</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"n_reads_hv"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_hv <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Identify high-ranking families and group others</span></span>
+<span><span class="va">hv_family_major_tab</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">&gt;</span> <span class="va">threshold_major_family</span><span class="op">)</span></span>
+<span><span class="va">hv_family_counts_major</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hv_family_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, p_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, </span>
+<span>                               levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hv_family_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">hv_family_counts_display</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts_major</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_hv</span>, classification <span class="op">=</span> <span class="va">name_display</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_hv_family</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">hv_family_counts_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% HV Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral family"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="st">"Family composition of human-viral reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">theme</span><span class="op">(</span>plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">1.4</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span>, face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_hv_family</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/hv-family-1.png" class="img-fluid figure-img" width="864"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>In investigating individual viral families, to avoid distortions from a few rare reads, I restricted myself to samples where that family made up at least 10% of human-viral reads.</p>
+<p>As usual, <em>Papillomaviridae</em> reads are divided among many different viral species. In this case, Betapapillomavirus 1 and 2 are the most prevalent across samples, but many other alpha-, beta-, gamma-, and mupapillomaviruses are highly prevalent in at least some samples.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb38"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">threshold_major_species</span> <span class="op">&lt;-</span> <span class="fl">0.4</span></span>
+<span><span class="va">taxid_papilloma</span> <span class="op">&lt;-</span> <span class="fl">151340</span></span>
+<span></span>
+<span><span class="co"># Get set of Papillomaviridae reads</span></span>
+<span><span class="va">papilloma_samples</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_papilloma</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">&gt;=</span> <span class="fl">0.1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span></span>
+<span><span class="va">papilloma_ids</span> <span class="op">&lt;-</span> <span class="va">hv_reads_family</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_papilloma</span>, <span class="va">sample</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">papilloma_samples</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">seq_id</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count reads for each Papillomaviridae species</span></span>
+<span><span class="va">papilloma_species_counts</span> <span class="op">&lt;-</span> <span class="va">hv_reads_species</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">seq_id</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">papilloma_ids</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">count</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"n_reads_hv"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_papilloma <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Identify high-ranking families and group others</span></span>
+<span><span class="va">papilloma_species_major_tab</span> <span class="op">&lt;-</span> <span class="va">papilloma_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_papilloma</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_papilloma</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_papilloma</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_papilloma</span> <span class="op">&gt;</span> <span class="va">threshold_major_species</span><span class="op">)</span></span>
+<span><span class="va">papilloma_species_counts_major</span> <span class="op">&lt;-</span> <span class="va">papilloma_species_counts</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">papilloma_species_major_tab</span><span class="op">$</span><span class="va">name</span>, </span>
+<span>                               <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_papilloma <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>,</span>
+<span>            p_reads_papilloma <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_papilloma</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, </span>
+<span>                               levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">papilloma_species_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">papilloma_species_counts_display</span> <span class="op">&lt;-</span> <span class="va">papilloma_species_counts_major</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_papilloma</span>, classification <span class="op">=</span> <span class="va">name_display</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_papilloma_species</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">papilloma_species_counts_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Papillomaviridae Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral species"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="st">"Species composition of Papillomaviridae reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">theme</span><span class="op">(</span>plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">1.4</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span>, face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_papilloma_species</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/hv-species-papilloma-1.png" class="img-fluid figure-img" width="864"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb39"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get most prominent species for text</span></span>
+<span><span class="va">papilloma_species_collate</span> <span class="op">&lt;-</span> <span class="va">papilloma_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_tot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, p_reads_mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_papilloma</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">n_reads_tot</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<p>In terms of total reads across samples, herpesviruses are dominated by Epstein-Barr virus (Human gammaherpesvirus 4), HSV-1 (Human alphaherpesvirus 1), and human cytomegalovirus (Human betaherpesvirus 5). However, numerous other herpesviruses are also present.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb40"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">threshold_major_species</span> <span class="op">&lt;-</span> <span class="fl">0.4</span></span>
+<span><span class="va">taxid_herpes</span> <span class="op">&lt;-</span> <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op">==</span> <span class="st">"Herpesviridae"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get set of herpesviridae reads</span></span>
+<span><span class="va">herpes_samples</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_herpes</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">&gt;=</span> <span class="fl">0.1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span></span>
+<span><span class="va">herpes_ids</span> <span class="op">&lt;-</span> <span class="va">hv_reads_family</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_herpes</span>, <span class="va">sample</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">herpes_samples</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">seq_id</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count reads for each herpesviridae species</span></span>
+<span><span class="va">herpes_species_counts</span> <span class="op">&lt;-</span> <span class="va">hv_reads_species</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">seq_id</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">herpes_ids</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">count</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"n_reads_hv"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_herpes <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Identify high-ranking families and group others</span></span>
+<span><span class="va">herpes_species_major_tab</span> <span class="op">&lt;-</span> <span class="va">herpes_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_herpes</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_herpes</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_herpes</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_herpes</span> <span class="op">&gt;</span> <span class="va">threshold_major_species</span><span class="op">)</span></span>
+<span><span class="va">herpes_species_counts_major</span> <span class="op">&lt;-</span> <span class="va">herpes_species_counts</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">herpes_species_major_tab</span><span class="op">$</span><span class="va">name</span>, </span>
+<span>                               <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_herpes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>,</span>
+<span>            p_reads_herpes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_herpes</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, </span>
+<span>                               levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">herpes_species_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">herpes_species_counts_display</span> <span class="op">&lt;-</span> <span class="va">herpes_species_counts_major</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_herpes</span>, classification <span class="op">=</span> <span class="va">name_display</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_herpes_species</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">herpes_species_counts_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% herpesviridae Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral species"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="st">"Species composition of Herpesviridae reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">theme</span><span class="op">(</span>plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">1.4</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span>, face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_herpes_species</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/hv-species-herpes-1.png" class="img-fluid figure-img" width="864"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb41"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get most prominent species for text</span></span>
+<span><span class="va">herpes_species_collate</span> <span class="op">&lt;-</span> <span class="va">herpes_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_tot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, p_reads_mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_herpes</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">n_reads_tot</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<p>In sharp contrast to the above, my pipeline classifies the great majority of anellovirus reads in all samples into a single species, torque teno virus. Looking online, it looks like there are a lot of “torque teno viruses” within <em>Anelloviridae</em> – for example, Wikipedia says that the genus <em>Alphatorquevirus</em> contains &gt;20 numbered torque teno viruses – so I’m not sure exactly which virus this refers to.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb42"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">threshold_major_species</span> <span class="op">&lt;-</span> <span class="fl">0.1</span></span>
+<span><span class="va">taxid_anello</span> <span class="op">&lt;-</span> <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op">==</span> <span class="st">"Anelloviridae"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get set of anelloviridae reads</span></span>
+<span><span class="va">anello_samples</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_anello</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">&gt;=</span> <span class="fl">0.1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span></span>
+<span><span class="va">anello_ids</span> <span class="op">&lt;-</span> <span class="va">hv_reads_family</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_anello</span>, <span class="va">sample</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">anello_samples</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">seq_id</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count reads for each anelloviridae species</span></span>
+<span><span class="va">anello_species_counts</span> <span class="op">&lt;-</span> <span class="va">hv_reads_species</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">seq_id</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">anello_ids</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">count</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"n_reads_hv"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_anello <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Identify high-ranking families and group others</span></span>
+<span><span class="va">anello_species_major_tab</span> <span class="op">&lt;-</span> <span class="va">anello_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_anello</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_anello</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_anello</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_anello</span> <span class="op">&gt;</span> <span class="va">threshold_major_species</span><span class="op">)</span></span>
+<span><span class="va">anello_species_counts_major</span> <span class="op">&lt;-</span> <span class="va">anello_species_counts</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">anello_species_major_tab</span><span class="op">$</span><span class="va">name</span>, </span>
+<span>                               <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_anello <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>,</span>
+<span>            p_reads_anello <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_anello</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, </span>
+<span>                               levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">anello_species_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">anello_species_counts_display</span> <span class="op">&lt;-</span> <span class="va">anello_species_counts_major</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_anello</span>, classification <span class="op">=</span> <span class="va">name_display</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_anello_species</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">anello_species_counts_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Anelloviridae Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral species"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="st">"Species composition of Anelloviridae reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">theme</span><span class="op">(</span>plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">1.4</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span>, face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_anello_species</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/hv-species-anello-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb43"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get most prominent species for text</span></span>
+<span><span class="va">anello_species_collate</span> <span class="op">&lt;-</span> <span class="va">anello_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_tot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, p_reads_mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_anello</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">n_reads_tot</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<p>Polyomaviruses are intermediate; most viruses are dominated by a single species, <em>Alphapolyomavirus quintihominis</em>, but several other viruses in the family are also present.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb44"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">threshold_major_species</span> <span class="op">&lt;-</span> <span class="fl">0.1</span></span>
+<span><span class="va">taxid_polyoma</span> <span class="op">&lt;-</span> <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op">==</span> <span class="st">"Polyomaviridae"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get set of polyomaviridae reads</span></span>
+<span><span class="co"># Get set of polyomaviridae reads</span></span>
+<span><span class="va">polyoma_samples</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_polyoma</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">&gt;=</span> <span class="fl">0.1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span></span>
+<span><span class="va">polyoma_ids</span> <span class="op">&lt;-</span> <span class="va">hv_reads_family</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_polyoma</span>, <span class="va">sample</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">polyoma_samples</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">seq_id</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count reads for each polyomaviridae species</span></span>
+<span><span class="va">polyoma_species_counts</span> <span class="op">&lt;-</span> <span class="va">hv_reads_species</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">seq_id</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">polyoma_ids</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">count</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"n_reads_hv"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_polyoma <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Identify high-ranking families and group others</span></span>
+<span><span class="va">polyoma_species_major_tab</span> <span class="op">&lt;-</span> <span class="va">polyoma_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_polyoma</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_polyoma</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_polyoma</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_polyoma</span> <span class="op">&gt;</span> <span class="va">threshold_major_species</span><span class="op">)</span></span>
+<span><span class="va">polyoma_species_counts_major</span> <span class="op">&lt;-</span> <span class="va">polyoma_species_counts</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">polyoma_species_major_tab</span><span class="op">$</span><span class="va">name</span>, </span>
+<span>                               <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_polyoma <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>,</span>
+<span>            p_reads_polyoma <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_polyoma</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, </span>
+<span>                               levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">polyoma_species_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">polyoma_species_counts_display</span> <span class="op">&lt;-</span> <span class="va">polyoma_species_counts_major</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_polyoma</span>, classification <span class="op">=</span> <span class="va">name_display</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_polyoma_species</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">polyoma_species_counts_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Polyomaviridae Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral species"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="st">"Species composition of Polyomaviridae reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">theme</span><span class="op">(</span>plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">1.4</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span>, face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_polyoma_species</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/hv-species-polyoma-1.png" class="img-fluid figure-img" width="864"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb45"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get most prominent species for text</span></span>
+<span><span class="va">polyoma_species_collate</span> <span class="op">&lt;-</span> <span class="va">polyoma_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_tot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, p_reads_mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_polyoma</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">n_reads_tot</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<p>Finally, poxvirus reads in most samples are dominated by molluscum contagiosum virus (which I expect to be real), followed by Orf virus (which I expect to be fake). These expectations are borne out by BLAST alignments (below).</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb46"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">threshold_major_species</span> <span class="op">&lt;-</span> <span class="fl">0.1</span></span>
+<span><span class="va">taxid_pox</span> <span class="op">&lt;-</span> <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op">==</span> <span class="st">"Poxviridae"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get set of poxviridae reads</span></span>
+<span><span class="co"># Get set of poxviridae reads</span></span>
+<span><span class="va">pox_samples</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_pox</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">&gt;=</span> <span class="fl">0.1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span></span>
+<span><span class="va">pox_ids</span> <span class="op">&lt;-</span> <span class="va">hv_reads_family</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">taxid_pox</span>, <span class="va">sample</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">pox_samples</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pull</span><span class="op">(</span><span class="va">seq_id</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count reads for each poxviridae species</span></span>
+<span><span class="va">pox_species_counts</span> <span class="op">&lt;-</span> <span class="va">hv_reads_species</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">seq_id</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">pox_ids</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">count</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"n_reads_hv"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_pox <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Identify high-ranking families and group others</span></span>
+<span><span class="va">pox_species_major_tab</span> <span class="op">&lt;-</span> <span class="va">pox_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_pox</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_pox</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_pox</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_pox</span> <span class="op">&gt;</span> <span class="va">threshold_major_species</span><span class="op">)</span></span>
+<span><span class="va">pox_species_counts_major</span> <span class="op">&lt;-</span> <span class="va">pox_species_counts</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">pox_species_major_tab</span><span class="op">$</span><span class="va">name</span>, </span>
+<span>                               <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_pox <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>,</span>
+<span>            p_reads_pox <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_pox</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, </span>
+<span>                               levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">pox_species_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">pox_species_counts_display</span> <span class="op">&lt;-</span> <span class="va">pox_species_counts_major</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_pox</span>, classification <span class="op">=</span> <span class="va">name_display</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_pox_species</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">pox_species_counts_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Poxviridae Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral species"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="st">"Species composition of Poxviridae reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">theme</span><span class="op">(</span>plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">1.4</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span>, face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_pox_species</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/hv-species-pox-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb47"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get most prominent species for text</span></span>
+<span><span class="va">pox_species_collate</span> <span class="op">&lt;-</span> <span class="va">pox_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_tot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, p_reads_mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_pox</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">n_reads_tot</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb48"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Configure</span></span>
+<span><span class="va">ref_taxids_hv</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">10279</span>, <span class="fl">10258</span><span class="op">)</span></span>
+<span><span class="va">ref_names_hv</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">ref_taxids_hv</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">x</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">first</span><span class="op">)</span></span>
+<span><span class="va">p_threshold</span> <span class="op">&lt;-</span> <span class="fl">0.1</span></span>
+<span></span>
+<span><span class="co"># Get taxon names</span></span>
+<span><span class="va">tax_names_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir_base</span>, <span class="st">"taxid-names.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">tax_names</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">tax_names_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Add missing names</span></span>
+<span><span class="va">tax_names_new</span> <span class="op">&lt;-</span> <span class="fu">tribble</span><span class="op">(</span><span class="op">~</span><span class="va">staxid</span>, <span class="op">~</span><span class="va">name</span>,</span>
+<span>                         <span class="fl">3050295</span>, <span class="st">"Cytomegalovirus humanbeta5"</span>,</span>
+<span>                         <span class="fl">459231</span>, <span class="st">"FLAG-tagging vector pFLAG97-TSR"</span>,</span>
+<span>                         <span class="fl">257877</span>, <span class="st">"Macaca thibetana thibetana"</span>,</span>
+<span>                         <span class="fl">256321</span>, <span class="st">"Lentiviral transfer vector pHsCXW"</span>,</span>
+<span>                         <span class="fl">419242</span>, <span class="st">"Shuttle vector pLvCmvMYOCDHA"</span>,</span>
+<span>                         <span class="fl">419243</span>, <span class="st">"Shuttle vector pLvCmvLacZ"</span>,</span>
+<span>                         <span class="fl">421868</span>, <span class="st">"Cloning vector pLvCmvLacZ.Gfp"</span>,</span>
+<span>                         <span class="fl">421869</span>, <span class="st">"Cloning vector pLvCmvMyocardin.Gfp"</span>,</span>
+<span>                         <span class="fl">426303</span>, <span class="st">"Lentiviral vector pNL-GFP-RRE(SA)"</span>,</span>
+<span>                         <span class="fl">436015</span>, <span class="st">"Lentiviral transfer vector pFTMGW"</span>,</span>
+<span>                         <span class="fl">454257</span>, <span class="st">"Shuttle vector pLvCmvMYOCD2aHA"</span>,</span>
+<span>                         <span class="fl">476184</span>, <span class="st">"Shuttle vector pLV.mMyoD::ERT2.eGFP"</span>,</span>
+<span>                         <span class="fl">476185</span>, <span class="st">"Shuttle vector pLV.hMyoD.eGFP"</span>,</span>
+<span>                         <span class="fl">591936</span>, <span class="st">"Piliocolobus tephrosceles"</span>,</span>
+<span>                         <span class="fl">627481</span>, <span class="st">"Lentiviral transfer vector pFTM3GW"</span>,</span>
+<span>                         <span class="fl">680261</span>, <span class="st">"Self-inactivating lentivirus vector pLV.C-EF1a.cyt-bGal.dCpG"</span>,</span>
+<span>                         <span class="fl">2952778</span>, <span class="st">"Expression vector pLV[Exp]-EGFP:T2A:Puro-EF1A"</span>,</span>
+<span>                         <span class="fl">3022699</span>, <span class="st">"Vector PAS_122122"</span>,</span>
+<span>                         <span class="fl">3025913</span>, <span class="st">"Vector pSIN-WP-mPGK-GDNF"</span>,</span>
+<span>                         <span class="fl">3105863</span>, <span class="st">"Vector pLKO.1-ZsGreen1"</span>,</span>
+<span>                         <span class="fl">3105864</span>, <span class="st">"Vector pLKO.1-ZsGreen1 mouse Wfs1 shRNA"</span>,</span>
+<span>                         <span class="fl">3108001</span>, <span class="st">"Cloning vector pLVSIN-CMV_Neo_v4.0"</span>,</span>
+<span>                         <span class="fl">3109234</span>, <span class="st">"Vector pTwist+Kan+High"</span>,</span>
+<span>                         <span class="fl">3117662</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;P301L"</span>,</span>
+<span>                         <span class="fl">3117663</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;P301L:T2A:mRuby3"</span>,</span>
+<span>                         <span class="fl">3117664</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;hMAPT[NM_005910.6](ns):T2A:mRuby3"</span>,</span>
+<span>                         <span class="fl">3117665</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;mRuby3"</span>,</span>
+<span>                         <span class="fl">3117666</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;mRuby3/NFAT3 fusion protein"</span>,</span>
+<span>                         <span class="fl">3117667</span>, <span class="st">"Cloning vector pLV[Exp]-Neo-mPGK&gt;{EGFP-hSEPT6}"</span>,</span>
+<span>                         <span class="fl">438045</span>, <span class="st">"Xenotropic MuLV-related virus"</span>,</span>
+<span>                         <span class="fl">447135</span>, <span class="st">"Myodes glareolus"</span>,</span>
+<span>                         <span class="fl">590745</span>, <span class="st">"Mus musculus mobilized endogenous polytropic provirus"</span>,</span>
+<span>                         <span class="fl">181858</span>, <span class="st">"Murine AIDS virus-related provirus"</span>,</span>
+<span>                         <span class="fl">356663</span>, <span class="st">"Xenotropic MuLV-related virus VP35"</span>,</span>
+<span>                         <span class="fl">356664</span>, <span class="st">"Xenotropic MuLV-related virus VP42"</span>,</span>
+<span>                         <span class="fl">373193</span>, <span class="st">"Xenotropic MuLV-related virus VP62"</span>,</span>
+<span>                         <span class="fl">286419</span>, <span class="st">"Canis lupus dingo"</span>,</span>
+<span>                         <span class="fl">415978</span>, <span class="st">"Sus scrofa scrofa"</span>,</span>
+<span>                         <span class="fl">494514</span>, <span class="st">"Vulpes lagopus"</span>,</span>
+<span>                         <span class="fl">3082113</span>, <span class="st">"Rangifer tarandus platyrhynchus"</span>,</span>
+<span>                         <span class="fl">3119969</span>, <span class="st">"Bubalus kerabau"</span><span class="op">)</span></span>
+<span><span class="va">tax_names</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">tax_names</span>, <span class="va">tax_names_new</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get matches</span></span>
+<span><span class="va">hv_blast_staxids</span> <span class="op">&lt;-</span> <span class="va">hv_reads_species</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">ref_taxids_hv</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>n_seq <span class="op">=</span> <span class="fu">n</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">blast_results_paired</span>, by<span class="op">=</span><span class="st">"seq_id"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>staxid <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html">as.integer</a></span><span class="op">(</span><span class="va">staxid</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">tax_names</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>sname<span class="op">=</span><span class="va">name</span><span class="op">)</span>, by<span class="op">=</span><span class="st">"staxid"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count matches</span></span>
+<span><span class="va">hv_blast_counts</span> <span class="op">&lt;-</span> <span class="va">hv_blast_staxids</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">taxid</span>, <span class="va">name</span>, <span class="va">staxid</span>, <span class="va">sname</span>, <span class="va">n_seq</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="va">count</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p<span class="op">=</span><span class="va">n</span><span class="op">/</span><span class="va">n_seq</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Subset to major matches</span></span>
+<span><span class="va">hv_blast_counts_major</span> <span class="op">&lt;-</span> <span class="va">hv_blast_counts</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">n</span><span class="op">&gt;</span><span class="fl">1</span>, <span class="va">p</span><span class="op">&gt;</span><span class="va">p_threshold</span>, <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">staxid</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">&lt;=</span> <span class="fl">25</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op">==</span> <span class="va">ref_names_hv</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>, <span class="st">"MCV"</span>, <span class="va">name</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_hv_blast</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">hv_blast_counts_major</span>, mapping<span class="op">=</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">p</span>, y<span class="op">=</span><span class="va">sname</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">name_display</span><span class="op">~</span><span class="va">.</span>, scales<span class="op">=</span><span class="st">"free_y"</span>, space<span class="op">=</span><span class="st">"free_y"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% mapped reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span><span class="op">)</span>, </span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>axis.title.y <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_hv_blast</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/hv-blast-hits-1.png" class="img-fluid figure-img" width="576"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>Finally, here again are the overall relative abundances of the specific viral genera I picked out manually in my last entry:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb49"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Define reference genera</span></span>
+<span><span class="va">path_genera_rna</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Mamastrovirus"</span>, <span class="st">"Enterovirus"</span>, <span class="st">"Salivirus"</span>, <span class="st">"Kobuvirus"</span>, <span class="st">"Norovirus"</span>, <span class="st">"Sapovirus"</span>, <span class="st">"Rotavirus"</span>, <span class="st">"Alphacoronavirus"</span>, <span class="st">"Betacoronavirus"</span>, <span class="st">"Alphainfluenzavirus"</span>, <span class="st">"Betainfluenzavirus"</span>, <span class="st">"Lentivirus"</span><span class="op">)</span></span>
+<span><span class="va">path_genera_dna</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Mastadenovirus"</span>, <span class="st">"Alphapolyomavirus"</span>, <span class="st">"Betapolyomavirus"</span>, <span class="st">"Alphapapillomavirus"</span>, <span class="st">"Betapapillomavirus"</span>, <span class="st">"Gammapapillomavirus"</span>, <span class="st">"Orthopoxvirus"</span>, <span class="st">"Simplexvirus"</span>,</span>
+<span>                     <span class="st">"Lymphocryptovirus"</span>, <span class="st">"Cytomegalovirus"</span>, <span class="st">"Dependoparvovirus"</span><span class="op">)</span></span>
+<span><span class="va">path_genera</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="fu">tibble</span><span class="op">(</span>name<span class="op">=</span><span class="va">path_genera_rna</span>, genome_type<span class="op">=</span><span class="st">"RNA genome"</span><span class="op">)</span>,</span>
+<span>                         <span class="fu">tibble</span><span class="op">(</span>name<span class="op">=</span><span class="va">path_genera_dna</span>, genome_type<span class="op">=</span><span class="st">"DNA genome"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">viral_taxa</span>, by<span class="op">=</span><span class="st">"name"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count in each sample</span></span>
+<span><span class="va">n_path_genera</span> <span class="op">&lt;-</span> <span class="va">hv_reads_genus</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date_alias</span>, <span class="va">city</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">count</span><span class="op">(</span>name<span class="op">=</span><span class="st">"n_reads_viral"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">path_genera</span>, by<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"taxid"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">read_counts_raw</span>, by<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"sample"</span>, <span class="st">"date_alias"</span>, <span class="st">"city"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_viral <span class="op">=</span> <span class="va">n_reads_viral</span><span class="op">/</span><span class="va">n_reads_raw</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Pivot out and back to add zero lines</span></span>
+<span><span class="va">n_path_genera_out</span> <span class="op">&lt;-</span> <span class="va">n_path_genera</span> <span class="op">%&gt;%</span> <span class="va">ungroup</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">name</span>, <span class="va">n_reads_viral</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_wider</span><span class="op">(</span>names_from<span class="op">=</span><span class="st">"name"</span>, values_from<span class="op">=</span><span class="st">"n_reads_viral"</span>, values_fill<span class="op">=</span><span class="fl">0</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_longer</span><span class="op">(</span><span class="op">-</span><span class="va">sample</span>, names_to<span class="op">=</span><span class="st">"name"</span>, values_to<span class="op">=</span><span class="st">"n_reads_viral"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">read_counts_raw</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">path_genera</span>, by<span class="op">=</span><span class="st">"name"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_viral <span class="op">=</span> <span class="va">n_reads_viral</span><span class="op">/</span><span class="va">n_reads_raw</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">## Aggregate across dates</span></span>
+<span><span class="va">n_path_genera_stype</span> <span class="op">&lt;-</span> <span class="va">n_path_genera_out</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">genome_type</span>, <span class="va">city</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_raw <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_raw</span><span class="op">)</span>,</span>
+<span>            n_reads_viral <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_viral</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>sample<span class="op">=</span><span class="st">"All samples"</span>, date<span class="op">=</span><span class="st">"All dates"</span>,</span>
+<span>         p_reads_viral <span class="op">=</span> <span class="va">n_reads_viral</span><span class="op">/</span><span class="va">n_reads_raw</span>,</span>
+<span>         na_type <span class="op">=</span> <span class="st">"DNA"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_path_genera</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">n_path_genera_stype</span>,</span>
+<span>                        <span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">name</span>, x<span class="op">=</span><span class="va">p_reads_viral</span>, color<span class="op">=</span><span class="va">city</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_log10</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Relative abundance"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_city</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">genome_type</span><span class="op">~</span><span class="va">.</span>, scales<span class="op">=</span><span class="st">"free_y"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>axis.title.y <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_path_genera</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output cell-output-stderr">
+<pre><code>Warning: Transformation introduced infinite values in continuous x-axis</code></pre>
+</div>
+<div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_leung_files/figure-html/ra-genera-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="conclusion" class="level1"><h1>Conclusion</h1>
+<p>This is the third, largest, and final of this tranche of air-sampling datasets that I’ve run through this pipeline. Many of the high-level findings were similar to Prussin and Rosario, including high relative abundance of human reads, low total viral reads, an absence of enteric viruses, and high abundance of papillomaviruses among human-infecting viruses.</p>
+<p>In the future, I’ll do a more in-depth comparative analysis across different datasets to compare the abundance of different viruses. For now, though, there are some major updates to the pipeline I want to make before I do any more public analyses.</p>
+
+
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+</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
+<div class="sourceCode" id="cb51" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Workflow analysis of Leung et al. (2021)"</span></span>
+<span id="cb51-3"><a href="#cb51-3" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> "Air sampling from a student dorm in Colorado."</span></span>
+<span id="cb51-4"><a href="#cb51-4" aria-hidden="true" tabindex="-1"></a><span class="an">author:</span><span class="co"> "Will Bradshaw"</span></span>
+<span id="cb51-5"><a href="#cb51-5" aria-hidden="true" tabindex="-1"></a><span class="an">date:</span><span class="co"> 2024-04-12</span></span>
+<span id="cb51-6"><a href="#cb51-6" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span>
+<span id="cb51-7"><a href="#cb51-7" aria-hidden="true" tabindex="-1"></a><span class="co">  html:</span></span>
+<span id="cb51-8"><a href="#cb51-8" aria-hidden="true" tabindex="-1"></a><span class="co">    code-fold: true</span></span>
+<span id="cb51-9"><a href="#cb51-9" aria-hidden="true" tabindex="-1"></a><span class="co">    code-tools: true</span></span>
+<span id="cb51-10"><a href="#cb51-10" aria-hidden="true" tabindex="-1"></a><span class="co">    code-link: true</span></span>
+<span id="cb51-11"><a href="#cb51-11" aria-hidden="true" tabindex="-1"></a><span class="co">    df-print: paged</span></span>
+<span id="cb51-12"><a href="#cb51-12" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
+<span id="cb51-13"><a href="#cb51-13" aria-hidden="true" tabindex="-1"></a><span class="an">title-block-banner:</span><span class="co"> black</span></span>
+<span id="cb51-14"><a href="#cb51-14" aria-hidden="true" tabindex="-1"></a><span class="an">draft:</span><span class="co"> true</span></span>
+<span id="cb51-15"><a href="#cb51-15" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb51-16"><a href="#cb51-16" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-19"><a href="#cb51-19" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-20"><a href="#cb51-20" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: load-packages</span></span>
+<span id="cb51-21"><a href="#cb51-21" aria-hidden="true" tabindex="-1"></a><span class="co">#| include: false</span></span>
+<span id="cb51-22"><a href="#cb51-22" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
+<span id="cb51-23"><a href="#cb51-23" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(cowplot)</span>
+<span id="cb51-24"><a href="#cb51-24" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(patchwork)</span>
+<span id="cb51-25"><a href="#cb51-25" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(fastqcr)</span>
+<span id="cb51-26"><a href="#cb51-26" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(RColorBrewer)</span>
+<span id="cb51-27"><a href="#cb51-27" aria-hidden="true" tabindex="-1"></a><span class="fu">source</span>(<span class="st">"../scripts/aux_plot-theme.R"</span>)</span>
+<span id="cb51-28"><a href="#cb51-28" aria-hidden="true" tabindex="-1"></a>theme_base <span class="ot">&lt;-</span> theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">aspect.ratio =</span> <span class="cn">NULL</span>)</span>
+<span id="cb51-29"><a href="#cb51-29" aria-hidden="true" tabindex="-1"></a>theme_rotate <span class="ot">&lt;-</span> theme_base <span class="sc">+</span> <span class="fu">theme</span>(</span>
+<span id="cb51-30"><a href="#cb51-30" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="dv">1</span>, <span class="at">angle =</span> <span class="dv">45</span>),</span>
+<span id="cb51-31"><a href="#cb51-31" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb51-32"><a href="#cb51-32" aria-hidden="true" tabindex="-1"></a>theme_kit <span class="ot">&lt;-</span> theme_rotate <span class="sc">+</span> <span class="fu">theme</span>(</span>
+<span id="cb51-33"><a href="#cb51-33" aria-hidden="true" tabindex="-1"></a>  <span class="at">axis.title.x =</span> <span class="fu">element_blank</span>(),</span>
+<span id="cb51-34"><a href="#cb51-34" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb51-35"><a href="#cb51-35" aria-hidden="true" tabindex="-1"></a>tnl <span class="ot">&lt;-</span> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)</span>
+<span id="cb51-36"><a href="#cb51-36" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-37"><a href="#cb51-37" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-38"><a href="#cb51-38" aria-hidden="true" tabindex="-1"></a>The last in our current run of air sampling datasets is <span class="co">[</span><span class="ot">Leung et al.</span><span class="co">](https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-021-01044-7)</span> (2021), a study of active air samples collected in public transit systems from six cities (Denver, Hong Kong, London, NYC, Oslo, Stockholm) from June to September 2017.</span>
+<span id="cb51-39"><a href="#cb51-39" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-40"><a href="#cb51-40" aria-hidden="true" tabindex="-1"></a>Samples from Denver originated from their rail and bus system; all other samples originated from metro systems. Collection took place during working days and working hours. Air samples were collected with the SASS 3100 Dry Air Samplers (filtration) for 30 min at a flowrate of 300 L/min using electret microfibrous filters. Filters were stationed at 1.5m above floor level, facing downward (to avoid direct deposition).</span>
+<span id="cb51-41"><a href="#cb51-41" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-42"><a href="#cb51-42" aria-hidden="true" tabindex="-1"></a>This was a DNA-sequencing study, focused on the bacterial microbiome and resistome. Sample processing followed an ideosyncratic protocol, where samples were pelleted and the pellet and supernatant were processed separately before being recombined for NA extraction and sequencing; I don't have a great understanding of how this is expected to affect the viral fraction. Samples were sequenced with Illumina HiSeqX 2x150bp.</span>
+<span id="cb51-43"><a href="#cb51-43" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-44"><a href="#cb51-44" aria-hidden="true" tabindex="-1"></a><span class="fu"># The raw data</span></span>
+<span id="cb51-45"><a href="#cb51-45" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-46"><a href="#cb51-46" aria-hidden="true" tabindex="-1"></a>In total, the Leung dataset comprised 293 samples:</span>
+<span id="cb51-47"><a href="#cb51-47" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-50"><a href="#cb51-50" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-51"><a href="#cb51-51" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb51-52"><a href="#cb51-52" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: import-qc-data</span></span>
+<span id="cb51-53"><a href="#cb51-53" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-54"><a href="#cb51-54" aria-hidden="true" tabindex="-1"></a><span class="co"># Importing the data is a bit more complicated this time as the samples are split across three pipeline runs</span></span>
+<span id="cb51-55"><a href="#cb51-55" aria-hidden="true" tabindex="-1"></a>data_dir_base <span class="ot">&lt;-</span> <span class="st">"../data/2024-04-12_leung"</span></span>
+<span id="cb51-56"><a href="#cb51-56" aria-hidden="true" tabindex="-1"></a>data_dirs <span class="ot">&lt;-</span> <span class="fu">paste</span>(data_dir_base, <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>), <span class="at">sep=</span><span class="st">"/"</span>)</span>
+<span id="cb51-57"><a href="#cb51-57" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-58"><a href="#cb51-58" aria-hidden="true" tabindex="-1"></a><span class="co"># Define geo relationships for filling in</span></span>
+<span id="cb51-59"><a href="#cb51-59" aria-hidden="true" tabindex="-1"></a>geo <span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>region, <span class="sc">~</span>country, <span class="sc">~</span>city,</span>
+<span id="cb51-60"><a href="#cb51-60" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Asia"</span>, <span class="st">"Hong Kong"</span>, <span class="st">"Hong Kong"</span>,</span>
+<span id="cb51-61"><a href="#cb51-61" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Europe"</span>, <span class="st">"Norway"</span>, <span class="st">"Oslo"</span>,</span>
+<span id="cb51-62"><a href="#cb51-62" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Europe"</span>, <span class="st">"Sweden"</span>, <span class="st">"Stockholm"</span>,</span>
+<span id="cb51-63"><a href="#cb51-63" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Europe"</span>, <span class="st">"United Kingdom"</span>, <span class="st">"London"</span>,</span>
+<span id="cb51-64"><a href="#cb51-64" aria-hidden="true" tabindex="-1"></a>               <span class="st">"North America"</span>, <span class="st">"USA"</span>, <span class="st">"New York City"</span>,</span>
+<span id="cb51-65"><a href="#cb51-65" aria-hidden="true" tabindex="-1"></a>               <span class="st">"North America"</span>, <span class="st">"USA"</span>, <span class="st">"Denver"</span>)</span>
+<span id="cb51-66"><a href="#cb51-66" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-67"><a href="#cb51-67" aria-hidden="true" tabindex="-1"></a><span class="co"># Data input paths</span></span>
+<span id="cb51-68"><a href="#cb51-68" aria-hidden="true" tabindex="-1"></a>libraries_paths <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dirs, <span class="st">"sample-metadata.csv"</span>)</span>
+<span id="cb51-69"><a href="#cb51-69" aria-hidden="true" tabindex="-1"></a>basic_stats_paths <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dirs, <span class="st">"qc_basic_stats.tsv.gz"</span>)</span>
+<span id="cb51-70"><a href="#cb51-70" aria-hidden="true" tabindex="-1"></a>adapter_stats_paths <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dirs, <span class="st">"qc_adapter_stats.tsv.gz"</span>)</span>
+<span id="cb51-71"><a href="#cb51-71" aria-hidden="true" tabindex="-1"></a>quality_base_stats_paths <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dirs, <span class="st">"qc_quality_base_stats.tsv.gz"</span>)</span>
+<span id="cb51-72"><a href="#cb51-72" aria-hidden="true" tabindex="-1"></a>quality_seq_stats_paths <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dirs, <span class="st">"qc_quality_sequence_stats.tsv.gz"</span>)</span>
+<span id="cb51-73"><a href="#cb51-73" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-74"><a href="#cb51-74" aria-hidden="true" tabindex="-1"></a><span class="co"># Import libraries and extract metadata from sample names</span></span>
+<span id="cb51-75"><a href="#cb51-75" aria-hidden="true" tabindex="-1"></a>libraries_raw <span class="ot">&lt;-</span> <span class="fu">lapply</span>(libraries_paths, read_csv, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-76"><a href="#cb51-76" aria-hidden="true" tabindex="-1"></a>  bind_rows</span>
+<span id="cb51-77"><a href="#cb51-77" aria-hidden="true" tabindex="-1"></a>libraries <span class="ot">&lt;-</span> libraries_raw <span class="sc">%&gt;%</span></span>
+<span id="cb51-78"><a href="#cb51-78" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Fix missing entries</span></span>
+<span id="cb51-79"><a href="#cb51-79" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">city =</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(city), <span class="fu">sub</span>(<span class="st">", .*"</span>, <span class="st">""</span>, location), city)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-80"><a href="#cb51-80" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(geo, <span class="at">by=</span><span class="st">"city"</span>, <span class="at">suffix =</span> <span class="fu">c</span>(<span class="st">""</span>, <span class="st">"_new"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-81"><a href="#cb51-81" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">region =</span> <span class="fu">ifelse</span>(region <span class="sc">==</span> <span class="st">"uncalculated"</span>, region_new, region),</span>
+<span id="cb51-82"><a href="#cb51-82" aria-hidden="true" tabindex="-1"></a>         <span class="at">country =</span> <span class="fu">ifelse</span>(country <span class="sc">==</span> <span class="st">"uncalculated"</span>, country_new, country)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-83"><a href="#cb51-83" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="sc">-</span>country_new, <span class="sc">-</span>region_new) <span class="sc">%&gt;%</span></span>
+<span id="cb51-84"><a href="#cb51-84" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add sample aliases</span></span>
+<span id="cb51-85"><a href="#cb51-85" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(city, date, location) <span class="sc">%&gt;%</span></span>
+<span id="cb51-86"><a href="#cb51-86" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(city, date) <span class="sc">%&gt;%</span></span>
+<span id="cb51-87"><a href="#cb51-87" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample_count =</span> <span class="fu">row_number</span>(),</span>
+<span id="cb51-88"><a href="#cb51-88" aria-hidden="true" tabindex="-1"></a>         <span class="at">date_alias =</span> <span class="fu">paste</span>(<span class="fu">as.character</span>(date), sample_count, <span class="at">sep=</span><span class="st">"_"</span>),</span>
+<span id="cb51-89"><a href="#cb51-89" aria-hidden="true" tabindex="-1"></a>         <span class="at">sample_alias =</span> <span class="fu">paste</span>(city, date_alias, <span class="at">sep=</span><span class="st">"_"</span>))</span>
+<span id="cb51-90"><a href="#cb51-90" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-91"><a href="#cb51-91" aria-hidden="true" tabindex="-1"></a>count_city <span class="ot">&lt;-</span> libraries <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(region, country, city) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-92"><a href="#cb51-92" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name=</span><span class="st">"n_samples"</span>)</span>
+<span id="cb51-93"><a href="#cb51-93" aria-hidden="true" tabindex="-1"></a>count_city</span>
+<span id="cb51-94"><a href="#cb51-94" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-95"><a href="#cb51-95" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-98"><a href="#cb51-98" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-99"><a href="#cb51-99" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: process-qc-data</span></span>
+<span id="cb51-100"><a href="#cb51-100" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-101"><a href="#cb51-101" aria-hidden="true" tabindex="-1"></a><span class="co"># Import QC data</span></span>
+<span id="cb51-102"><a href="#cb51-102" aria-hidden="true" tabindex="-1"></a>stages <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"raw_concat"</span>, <span class="st">"cleaned"</span>, <span class="st">"dedup"</span>, <span class="st">"ribo_initial"</span>, <span class="st">"ribo_secondary"</span>)</span>
+<span id="cb51-103"><a href="#cb51-103" aria-hidden="true" tabindex="-1"></a>import_basic <span class="ot">&lt;-</span> <span class="cf">function</span>(paths){</span>
+<span id="cb51-104"><a href="#cb51-104" aria-hidden="true" tabindex="-1"></a>  <span class="fu">lapply</span>(paths, read_tsv, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span> bind_rows <span class="sc">%&gt;%</span></span>
+<span id="cb51-105"><a href="#cb51-105" aria-hidden="true" tabindex="-1"></a>    <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(sample) <span class="sc">%&gt;%</span></span>
+<span id="cb51-106"><a href="#cb51-106" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">stage =</span> <span class="fu">factor</span>(stage, <span class="at">levels =</span> stages),</span>
+<span id="cb51-107"><a href="#cb51-107" aria-hidden="true" tabindex="-1"></a>           <span class="at">sample =</span> <span class="fu">fct_inorder</span>(sample))</span>
+<span id="cb51-108"><a href="#cb51-108" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-109"><a href="#cb51-109" aria-hidden="true" tabindex="-1"></a>import_basic_paired <span class="ot">&lt;-</span> <span class="cf">function</span>(paths){</span>
+<span id="cb51-110"><a href="#cb51-110" aria-hidden="true" tabindex="-1"></a>  <span class="fu">import_basic</span>(paths) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(read_pair) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-111"><a href="#cb51-111" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">read_pair =</span> <span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(read_pair)))</span>
+<span id="cb51-112"><a href="#cb51-112" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-113"><a href="#cb51-113" aria-hidden="true" tabindex="-1"></a>basic_stats <span class="ot">&lt;-</span> <span class="fu">import_basic</span>(basic_stats_paths)</span>
+<span id="cb51-114"><a href="#cb51-114" aria-hidden="true" tabindex="-1"></a>adapter_stats <span class="ot">&lt;-</span> <span class="fu">import_basic_paired</span>(adapter_stats_paths)</span>
+<span id="cb51-115"><a href="#cb51-115" aria-hidden="true" tabindex="-1"></a>quality_base_stats <span class="ot">&lt;-</span> <span class="fu">import_basic_paired</span>(quality_base_stats_paths)</span>
+<span id="cb51-116"><a href="#cb51-116" aria-hidden="true" tabindex="-1"></a>quality_seq_stats <span class="ot">&lt;-</span> <span class="fu">import_basic_paired</span>(quality_seq_stats_paths)</span>
+<span id="cb51-117"><a href="#cb51-117" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-118"><a href="#cb51-118" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter to raw data</span></span>
+<span id="cb51-119"><a href="#cb51-119" aria-hidden="true" tabindex="-1"></a>basic_stats_raw <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb51-120"><a href="#cb51-120" aria-hidden="true" tabindex="-1"></a>adapter_stats_raw <span class="ot">&lt;-</span> adapter_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb51-121"><a href="#cb51-121" aria-hidden="true" tabindex="-1"></a>quality_base_stats_raw <span class="ot">&lt;-</span> quality_base_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb51-122"><a href="#cb51-122" aria-hidden="true" tabindex="-1"></a>quality_seq_stats_raw <span class="ot">&lt;-</span> quality_seq_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb51-123"><a href="#cb51-123" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-124"><a href="#cb51-124" aria-hidden="true" tabindex="-1"></a><span class="co"># Get key values for readout</span></span>
+<span id="cb51-125"><a href="#cb51-125" aria-hidden="true" tabindex="-1"></a>raw_read_counts <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span> ungroup <span class="sc">%&gt;%</span> </span>
+<span id="cb51-126"><a href="#cb51-126" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">rmin =</span> <span class="fu">min</span>(n_read_pairs), <span class="at">rmax=</span><span class="fu">max</span>(n_read_pairs),</span>
+<span id="cb51-127"><a href="#cb51-127" aria-hidden="true" tabindex="-1"></a>            <span class="at">rmean=</span><span class="fu">mean</span>(n_read_pairs), </span>
+<span id="cb51-128"><a href="#cb51-128" aria-hidden="true" tabindex="-1"></a>            <span class="at">rtot =</span> <span class="fu">sum</span>(n_read_pairs),</span>
+<span id="cb51-129"><a href="#cb51-129" aria-hidden="true" tabindex="-1"></a>            <span class="at">btot =</span> <span class="fu">sum</span>(n_bases_approx),</span>
+<span id="cb51-130"><a href="#cb51-130" aria-hidden="true" tabindex="-1"></a>            <span class="at">dmin =</span> <span class="fu">min</span>(percent_duplicates), <span class="at">dmax=</span><span class="fu">max</span>(percent_duplicates),</span>
+<span id="cb51-131"><a href="#cb51-131" aria-hidden="true" tabindex="-1"></a>            <span class="at">dmean=</span><span class="fu">mean</span>(percent_duplicates), <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb51-132"><a href="#cb51-132" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-133"><a href="#cb51-133" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-134"><a href="#cb51-134" aria-hidden="true" tabindex="-1"></a>These 293 samples yielded 0.39M-7.86M (mean 4.57M) reads per sample, for a total of 1.34B read pairs (402 gigabases of sequence). Read qualities were high at the 5' end but dropped off significantly in some samples, in definite need of cleaning. Adapter levels were high. With the exception of a couple of early samples, inferred duplication levels were low (mean 9.4%).</span>
+<span id="cb51-135"><a href="#cb51-135" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-138"><a href="#cb51-138" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-139"><a href="#cb51-139" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 9</span></span>
+<span id="cb51-140"><a href="#cb51-140" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb51-141"><a href="#cb51-141" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-basic-stats</span></span>
+<span id="cb51-142"><a href="#cb51-142" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-143"><a href="#cb51-143" aria-hidden="true" tabindex="-1"></a><span class="co"># Prepare data</span></span>
+<span id="cb51-144"><a href="#cb51-144" aria-hidden="true" tabindex="-1"></a>basic_stats_raw_metrics <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span></span>
+<span id="cb51-145"><a href="#cb51-145" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, city, date,</span>
+<span id="cb51-146"><a href="#cb51-146" aria-hidden="true" tabindex="-1"></a>         <span class="st">`</span><span class="at"># Read pairs</span><span class="st">`</span> <span class="ot">=</span> n_read_pairs,</span>
+<span id="cb51-147"><a href="#cb51-147" aria-hidden="true" tabindex="-1"></a>         <span class="st">`</span><span class="at">Total base pairs</span><span class="sc">\n</span><span class="at">(approx)</span><span class="st">`</span> <span class="ot">=</span> n_bases_approx,</span>
+<span id="cb51-148"><a href="#cb51-148" aria-hidden="true" tabindex="-1"></a>         <span class="st">`</span><span class="at">% Duplicates</span><span class="sc">\n</span><span class="at">(FASTQC)</span><span class="st">`</span> <span class="ot">=</span> percent_duplicates) <span class="sc">%&gt;%</span></span>
+<span id="cb51-149"><a href="#cb51-149" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="sc">-</span>(sample<span class="sc">:</span>date), <span class="at">names_to =</span> <span class="st">"metric"</span>, <span class="at">values_to =</span> <span class="st">"value"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-150"><a href="#cb51-150" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">metric =</span> <span class="fu">fct_inorder</span>(metric))</span>
+<span id="cb51-151"><a href="#cb51-151" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-152"><a href="#cb51-152" aria-hidden="true" tabindex="-1"></a><span class="co"># Set up plot templates</span></span>
+<span id="cb51-153"><a href="#cb51-153" aria-hidden="true" tabindex="-1"></a>scale_fill_city <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_fill_brewer, <span class="at">palette=</span><span class="st">"Set1"</span>,</span>
+<span id="cb51-154"><a href="#cb51-154" aria-hidden="true" tabindex="-1"></a>                                  <span class="at">name=</span><span class="st">"City"</span>)</span>
+<span id="cb51-155"><a href="#cb51-155" aria-hidden="true" tabindex="-1"></a>scale_x_cdate <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_x_date, <span class="at">name=</span><span class="st">"Collection Date"</span>,</span>
+<span id="cb51-156"><a href="#cb51-156" aria-hidden="true" tabindex="-1"></a>                                <span class="at">date_breaks =</span> <span class="st">"1 month"</span>, <span class="at">date_labels =</span> <span class="st">"%Y-%m-%d"</span>)</span>
+<span id="cb51-157"><a href="#cb51-157" aria-hidden="true" tabindex="-1"></a>g_basic <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(basic_stats_raw_metrics, </span>
+<span id="cb51-158"><a href="#cb51-158" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">y=</span>value, <span class="at">fill=</span>city, <span class="at">group=</span><span class="fu">interaction</span>(city,sample))) <span class="sc">+</span></span>
+<span id="cb51-159"><a href="#cb51-159" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">"dodge"</span>) <span class="sc">+</span></span>
+<span id="cb51-160"><a href="#cb51-160" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_cdate</span>() <span class="sc">+</span></span>
+<span id="cb51-161"><a href="#cb51-161" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-162"><a href="#cb51-162" aria-hidden="true" tabindex="-1"></a>  <span class="fu">expand_limits</span>(<span class="at">y=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">100</span>)) <span class="sc">+</span></span>
+<span id="cb51-163"><a href="#cb51-163" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_city</span>() <span class="sc">+</span> </span>
+<span id="cb51-164"><a href="#cb51-164" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(metric<span class="sc">~</span>., <span class="at">scales =</span> <span class="st">"free"</span>, <span class="at">space=</span><span class="st">"free_x"</span>, <span class="at">switch=</span><span class="st">"y"</span>) <span class="sc">+</span></span>
+<span id="cb51-165"><a href="#cb51-165" aria-hidden="true" tabindex="-1"></a>  theme_rotate <span class="sc">+</span> <span class="fu">theme</span>(</span>
+<span id="cb51-166"><a href="#cb51-166" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.title.y =</span> <span class="fu">element_blank</span>(),</span>
+<span id="cb51-167"><a href="#cb51-167" aria-hidden="true" tabindex="-1"></a>    <span class="at">strip.text.y =</span> <span class="fu">element_text</span>(<span class="at">face=</span><span class="st">"plain"</span>)</span>
+<span id="cb51-168"><a href="#cb51-168" aria-hidden="true" tabindex="-1"></a>  )</span>
+<span id="cb51-169"><a href="#cb51-169" aria-hidden="true" tabindex="-1"></a>g_basic</span>
+<span id="cb51-170"><a href="#cb51-170" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-171"><a href="#cb51-171" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-174"><a href="#cb51-174" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-175"><a href="#cb51-175" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-raw-quality</span></span>
+<span id="cb51-176"><a href="#cb51-176" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-177"><a href="#cb51-177" aria-hidden="true" tabindex="-1"></a><span class="co"># Set up plotting templates</span></span>
+<span id="cb51-178"><a href="#cb51-178" aria-hidden="true" tabindex="-1"></a>scale_color_city <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_color_brewer, <span class="at">palette=</span><span class="st">"Set1"</span>,</span>
+<span id="cb51-179"><a href="#cb51-179" aria-hidden="true" tabindex="-1"></a>                                   <span class="at">name=</span><span class="st">"City"</span>)</span>
+<span id="cb51-180"><a href="#cb51-180" aria-hidden="true" tabindex="-1"></a>g_qual_raw <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">mapping=</span><span class="fu">aes</span>(<span class="at">color=</span>city, <span class="at">linetype=</span>read_pair, </span>
+<span id="cb51-181"><a href="#cb51-181" aria-hidden="true" tabindex="-1"></a>                         <span class="at">group=</span><span class="fu">interaction</span>(sample,read_pair))) <span class="sc">+</span> </span>
+<span id="cb51-182"><a href="#cb51-182" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_city</span>() <span class="sc">+</span> <span class="fu">scale_linetype_discrete</span>(<span class="at">name =</span> <span class="st">"Read Pair"</span>) <span class="sc">+</span></span>
+<span id="cb51-183"><a href="#cb51-183" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color=</span><span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>),</span>
+<span id="cb51-184"><a href="#cb51-184" aria-hidden="true" tabindex="-1"></a>         <span class="at">linetype =</span> <span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>)) <span class="sc">+</span></span>
+<span id="cb51-185"><a href="#cb51-185" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb51-186"><a href="#cb51-186" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-187"><a href="#cb51-187" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize adapters</span></span>
+<span id="cb51-188"><a href="#cb51-188" aria-hidden="true" tabindex="-1"></a>g_adapters_raw <span class="ot">&lt;-</span> g_qual_raw <span class="sc">+</span> </span>
+<span id="cb51-189"><a href="#cb51-189" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>pc_adapters), <span class="at">data=</span>adapter_stats_raw) <span class="sc">+</span></span>
+<span id="cb51-190"><a href="#cb51-190" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Adapters"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb51-191"><a href="#cb51-191" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-192"><a href="#cb51-192" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb51-193"><a href="#cb51-193" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-194"><a href="#cb51-194" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(.<span class="sc">~</span>adapter)</span>
+<span id="cb51-195"><a href="#cb51-195" aria-hidden="true" tabindex="-1"></a>g_adapters_raw</span>
+<span id="cb51-196"><a href="#cb51-196" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-197"><a href="#cb51-197" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize quality</span></span>
+<span id="cb51-198"><a href="#cb51-198" aria-hidden="true" tabindex="-1"></a>g_quality_base_raw <span class="ot">&lt;-</span> g_qual_raw <span class="sc">+</span></span>
+<span id="cb51-199"><a href="#cb51-199" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-200"><a href="#cb51-200" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-201"><a href="#cb51-201" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>mean_phred_score), <span class="at">data=</span>quality_base_stats_raw) <span class="sc">+</span></span>
+<span id="cb51-202"><a href="#cb51-202" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">10</span>,<span class="dv">45</span>)) <span class="sc">+</span></span>
+<span id="cb51-203"><a href="#cb51-203" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb51-204"><a href="#cb51-204" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))</span>
+<span id="cb51-205"><a href="#cb51-205" aria-hidden="true" tabindex="-1"></a>g_quality_base_raw</span>
+<span id="cb51-206"><a href="#cb51-206" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-207"><a href="#cb51-207" aria-hidden="true" tabindex="-1"></a>g_quality_seq_raw <span class="ot">&lt;-</span> g_qual_raw <span class="sc">+</span></span>
+<span id="cb51-208"><a href="#cb51-208" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-209"><a href="#cb51-209" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-210"><a href="#cb51-210" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>mean_phred_score, <span class="at">y=</span>n_sequences), <span class="at">data=</span>quality_seq_stats_raw) <span class="sc">+</span></span>
+<span id="cb51-211"><a href="#cb51-211" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-212"><a href="#cb51-212" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Sequences"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))</span>
+<span id="cb51-213"><a href="#cb51-213" aria-hidden="true" tabindex="-1"></a>g_quality_seq_raw</span>
+<span id="cb51-214"><a href="#cb51-214" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-215"><a href="#cb51-215" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-216"><a href="#cb51-216" aria-hidden="true" tabindex="-1"></a><span class="fu"># Preprocessing</span></span>
+<span id="cb51-217"><a href="#cb51-217" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-218"><a href="#cb51-218" aria-hidden="true" tabindex="-1"></a>The average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. Read loss during cleaning was highly variable but averaged 11%, with a further \~7% lost during deduplication and \~0.3% during ribodepletion.</span>
+<span id="cb51-219"><a href="#cb51-219" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-222"><a href="#cb51-222" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-223"><a href="#cb51-223" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: preproc-table</span></span>
+<span id="cb51-224"><a href="#cb51-224" aria-hidden="true" tabindex="-1"></a>n_reads_rel <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> </span>
+<span id="cb51-225"><a href="#cb51-225" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, date, city, stage, </span>
+<span id="cb51-226"><a href="#cb51-226" aria-hidden="true" tabindex="-1"></a>         percent_duplicates, n_read_pairs) <span class="sc">%&gt;%</span></span>
+<span id="cb51-227"><a href="#cb51-227" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(sample, stage) <span class="sc">%&gt;%</span></span>
+<span id="cb51-228"><a href="#cb51-228" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_retained =</span> <span class="fu">replace_na</span>(n_read_pairs <span class="sc">/</span> <span class="fu">lag</span>(n_read_pairs), <span class="dv">0</span>),</span>
+<span id="cb51-229"><a href="#cb51-229" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_lost =</span> <span class="dv">1</span> <span class="sc">-</span> p_reads_retained,</span>
+<span id="cb51-230"><a href="#cb51-230" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_retained_abs =</span> n_read_pairs <span class="sc">/</span> n_read_pairs[<span class="dv">1</span>],</span>
+<span id="cb51-231"><a href="#cb51-231" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_lost_abs =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_retained_abs,</span>
+<span id="cb51-232"><a href="#cb51-232" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_lost_abs_marginal =</span> <span class="fu">replace_na</span>(p_reads_lost_abs <span class="sc">-</span> <span class="fu">lag</span>(p_reads_lost_abs), <span class="dv">0</span>))</span>
+<span id="cb51-233"><a href="#cb51-233" aria-hidden="true" tabindex="-1"></a>n_reads_rel_display <span class="ot">&lt;-</span> n_reads_rel <span class="sc">%&gt;%</span> </span>
+<span id="cb51-234"><a href="#cb51-234" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">Stage=</span>stage, <span class="at">City=</span>city) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-235"><a href="#cb51-235" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Stage) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-236"><a href="#cb51-236" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="st">`</span><span class="at">% Total Reads Lost (Cumulative)</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">paste0</span>(<span class="fu">round</span>(<span class="fu">min</span>(p_reads_lost_abs<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">"-"</span>, <span class="fu">round</span>(<span class="fu">max</span>(p_reads_lost_abs<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">" (mean "</span>, <span class="fu">round</span>(<span class="fu">mean</span>(p_reads_lost_abs<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">")"</span>),</span>
+<span id="cb51-237"><a href="#cb51-237" aria-hidden="true" tabindex="-1"></a>            <span class="st">`</span><span class="at">% Total Reads Lost (Marginal)</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">paste0</span>(<span class="fu">round</span>(<span class="fu">min</span>(p_reads_lost_abs_marginal<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">"-"</span>, <span class="fu">round</span>(<span class="fu">max</span>(p_reads_lost_abs_marginal<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">" (mean "</span>, <span class="fu">round</span>(<span class="fu">mean</span>(p_reads_lost_abs_marginal<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">")"</span>), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-238"><a href="#cb51-238" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(Stage <span class="sc">!=</span> <span class="st">"raw_concat"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-239"><a href="#cb51-239" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Stage =</span> Stage <span class="sc">%&gt;%</span> as.numeric <span class="sc">%&gt;%</span> <span class="fu">factor</span>(<span class="at">labels=</span><span class="fu">c</span>(<span class="st">"Trimming &amp; filtering"</span>, <span class="st">"Deduplication"</span>, <span class="st">"Initial ribodepletion"</span>, <span class="st">"Secondary ribodepletion"</span>)))</span>
+<span id="cb51-240"><a href="#cb51-240" aria-hidden="true" tabindex="-1"></a>n_reads_rel_display</span>
+<span id="cb51-241"><a href="#cb51-241" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-242"><a href="#cb51-242" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-245"><a href="#cb51-245" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-246"><a href="#cb51-246" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: preproc-figures</span></span>
+<span id="cb51-247"><a href="#cb51-247" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb51-248"><a href="#cb51-248" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-249"><a href="#cb51-249" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 6</span></span>
+<span id="cb51-250"><a href="#cb51-250" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-251"><a href="#cb51-251" aria-hidden="true" tabindex="-1"></a>g_stage_trace <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(basic_stats, <span class="fu">aes</span>(<span class="at">x=</span>stage, <span class="at">color=</span>city, <span class="at">group=</span>sample)) <span class="sc">+</span></span>
+<span id="cb51-252"><a href="#cb51-252" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_city</span>() <span class="sc">+</span></span>
+<span id="cb51-253"><a href="#cb51-253" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>city, <span class="at">scales=</span><span class="st">"free"</span>, <span class="at">ncol=</span><span class="dv">3</span>) <span class="sc">+</span></span>
+<span id="cb51-254"><a href="#cb51-254" aria-hidden="true" tabindex="-1"></a>  theme_kit <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)</span>
+<span id="cb51-255"><a href="#cb51-255" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-256"><a href="#cb51-256" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot reads over preprocessing</span></span>
+<span id="cb51-257"><a href="#cb51-257" aria-hidden="true" tabindex="-1"></a>g_reads_stages <span class="ot">&lt;-</span> g_stage_trace <span class="sc">+</span></span>
+<span id="cb51-258"><a href="#cb51-258" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>n_read_pairs)) <span class="sc">+</span></span>
+<span id="cb51-259"><a href="#cb51-259" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"# Read pairs"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>))</span>
+<span id="cb51-260"><a href="#cb51-260" aria-hidden="true" tabindex="-1"></a>g_reads_stages</span>
+<span id="cb51-261"><a href="#cb51-261" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-262"><a href="#cb51-262" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot relative read losses during preprocessing</span></span>
+<span id="cb51-263"><a href="#cb51-263" aria-hidden="true" tabindex="-1"></a>g_reads_rel <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(n_reads_rel, <span class="fu">aes</span>(<span class="at">x=</span>stage, <span class="at">color=</span>city, <span class="at">group=</span>sample)) <span class="sc">+</span></span>
+<span id="cb51-264"><a href="#cb51-264" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>p_reads_lost_abs_marginal)) <span class="sc">+</span></span>
+<span id="cb51-265"><a href="#cb51-265" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"% Total Reads Lost"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), </span>
+<span id="cb51-266"><a href="#cb51-266" aria-hidden="true" tabindex="-1"></a>                     <span class="at">labels =</span> <span class="cf">function</span>(x) x<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-267"><a href="#cb51-267" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_city</span>() <span class="sc">+</span></span>
+<span id="cb51-268"><a href="#cb51-268" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>city, <span class="at">scales=</span><span class="st">"free"</span>, <span class="at">ncol=</span><span class="dv">3</span>) <span class="sc">+</span></span>
+<span id="cb51-269"><a href="#cb51-269" aria-hidden="true" tabindex="-1"></a>  theme_kit <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)</span>
+<span id="cb51-270"><a href="#cb51-270" aria-hidden="true" tabindex="-1"></a>g_reads_rel</span>
+<span id="cb51-271"><a href="#cb51-271" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-272"><a href="#cb51-272" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-273"><a href="#cb51-273" aria-hidden="true" tabindex="-1"></a>Data cleaning was very successful at removing adapters and improving read qualities:</span>
+<span id="cb51-274"><a href="#cb51-274" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-277"><a href="#cb51-277" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-278"><a href="#cb51-278" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb51-279"><a href="#cb51-279" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-quality</span></span>
+<span id="cb51-280"><a href="#cb51-280" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 7</span></span>
+<span id="cb51-281"><a href="#cb51-281" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-282"><a href="#cb51-282" aria-hidden="true" tabindex="-1"></a>g_qual <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">mapping=</span><span class="fu">aes</span>(<span class="at">color=</span>city, <span class="at">linetype=</span>read_pair, </span>
+<span id="cb51-283"><a href="#cb51-283" aria-hidden="true" tabindex="-1"></a>                         <span class="at">group=</span><span class="fu">interaction</span>(sample,read_pair))) <span class="sc">+</span> </span>
+<span id="cb51-284"><a href="#cb51-284" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_city</span>() <span class="sc">+</span> <span class="fu">scale_linetype_discrete</span>(<span class="at">name =</span> <span class="st">"Read Pair"</span>) <span class="sc">+</span></span>
+<span id="cb51-285"><a href="#cb51-285" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color=</span><span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>),</span>
+<span id="cb51-286"><a href="#cb51-286" aria-hidden="true" tabindex="-1"></a>         <span class="at">linetype =</span> <span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>)) <span class="sc">+</span></span>
+<span id="cb51-287"><a href="#cb51-287" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb51-288"><a href="#cb51-288" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-289"><a href="#cb51-289" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize adapters</span></span>
+<span id="cb51-290"><a href="#cb51-290" aria-hidden="true" tabindex="-1"></a>g_adapters <span class="ot">&lt;-</span> g_qual <span class="sc">+</span> </span>
+<span id="cb51-291"><a href="#cb51-291" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>pc_adapters), <span class="at">data=</span>adapter_stats) <span class="sc">+</span></span>
+<span id="cb51-292"><a href="#cb51-292" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Adapters"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">20</span>),</span>
+<span id="cb51-293"><a href="#cb51-293" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">50</span>,<span class="dv">10</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-294"><a href="#cb51-294" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb51-295"><a href="#cb51-295" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-296"><a href="#cb51-296" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(stage<span class="sc">~</span>adapter)</span>
+<span id="cb51-297"><a href="#cb51-297" aria-hidden="true" tabindex="-1"></a>g_adapters</span>
+<span id="cb51-298"><a href="#cb51-298" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-299"><a href="#cb51-299" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize quality</span></span>
+<span id="cb51-300"><a href="#cb51-300" aria-hidden="true" tabindex="-1"></a>g_quality_base <span class="ot">&lt;-</span> g_qual <span class="sc">+</span></span>
+<span id="cb51-301"><a href="#cb51-301" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-302"><a href="#cb51-302" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-303"><a href="#cb51-303" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>mean_phred_score), <span class="at">data=</span>quality_base_stats) <span class="sc">+</span></span>
+<span id="cb51-304"><a href="#cb51-304" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">10</span>,<span class="dv">45</span>)) <span class="sc">+</span></span>
+<span id="cb51-305"><a href="#cb51-305" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb51-306"><a href="#cb51-306" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-307"><a href="#cb51-307" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(stage<span class="sc">~</span>.)</span>
+<span id="cb51-308"><a href="#cb51-308" aria-hidden="true" tabindex="-1"></a>g_quality_base</span>
+<span id="cb51-309"><a href="#cb51-309" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-310"><a href="#cb51-310" aria-hidden="true" tabindex="-1"></a>g_quality_seq <span class="ot">&lt;-</span> g_qual <span class="sc">+</span></span>
+<span id="cb51-311"><a href="#cb51-311" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-312"><a href="#cb51-312" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-313"><a href="#cb51-313" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>mean_phred_score, <span class="at">y=</span>n_sequences), <span class="at">data=</span>quality_seq_stats) <span class="sc">+</span></span>
+<span id="cb51-314"><a href="#cb51-314" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-315"><a href="#cb51-315" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Sequences"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-316"><a href="#cb51-316" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(stage<span class="sc">~</span>.)</span>
+<span id="cb51-317"><a href="#cb51-317" aria-hidden="true" tabindex="-1"></a>g_quality_seq</span>
+<span id="cb51-318"><a href="#cb51-318" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-319"><a href="#cb51-319" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-320"><a href="#cb51-320" aria-hidden="true" tabindex="-1"></a>According to FASTQC, cleaning + deduplication was very effective at reducing measured duplicate levels, which fell from an average of 9.4% to 1.7% for DNA reads:</span>
+<span id="cb51-321"><a href="#cb51-321" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-324"><a href="#cb51-324" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-325"><a href="#cb51-325" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: preproc-dedup</span></span>
+<span id="cb51-326"><a href="#cb51-326" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-327"><a href="#cb51-327" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 6</span></span>
+<span id="cb51-328"><a href="#cb51-328" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-329"><a href="#cb51-329" aria-hidden="true" tabindex="-1"></a>stage_dup <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(stage) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-330"><a href="#cb51-330" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">dmin =</span> <span class="fu">min</span>(percent_duplicates), <span class="at">dmax=</span><span class="fu">max</span>(percent_duplicates),</span>
+<span id="cb51-331"><a href="#cb51-331" aria-hidden="true" tabindex="-1"></a>            <span class="at">dmean=</span><span class="fu">mean</span>(percent_duplicates), <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb51-332"><a href="#cb51-332" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-333"><a href="#cb51-333" aria-hidden="true" tabindex="-1"></a>g_dup_stages <span class="ot">&lt;-</span> g_stage_trace <span class="sc">+</span></span>
+<span id="cb51-334"><a href="#cb51-334" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>percent_duplicates)) <span class="sc">+</span></span>
+<span id="cb51-335"><a href="#cb51-335" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"% Duplicates"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))</span>
+<span id="cb51-336"><a href="#cb51-336" aria-hidden="true" tabindex="-1"></a>g_dup_stages</span>
+<span id="cb51-337"><a href="#cb51-337" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-338"><a href="#cb51-338" aria-hidden="true" tabindex="-1"></a>g_readlen_stages <span class="ot">&lt;-</span> g_stage_trace <span class="sc">+</span> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>mean_seq_len)) <span class="sc">+</span></span>
+<span id="cb51-339"><a href="#cb51-339" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"Mean read length (nt)"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>))</span>
+<span id="cb51-340"><a href="#cb51-340" aria-hidden="true" tabindex="-1"></a>g_readlen_stages</span>
+<span id="cb51-341"><a href="#cb51-341" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-342"><a href="#cb51-342" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-343"><a href="#cb51-343" aria-hidden="true" tabindex="-1"></a><span class="fu"># High-level composition</span></span>
+<span id="cb51-344"><a href="#cb51-344" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-345"><a href="#cb51-345" aria-hidden="true" tabindex="-1"></a>As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:</span>
+<span id="cb51-346"><a href="#cb51-346" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-349"><a href="#cb51-349" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-350"><a href="#cb51-350" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: prepare-composition</span></span>
+<span id="cb51-351"><a href="#cb51-351" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-352"><a href="#cb51-352" aria-hidden="true" tabindex="-1"></a><span class="co"># Import Bracken data</span></span>
+<span id="cb51-353"><a href="#cb51-353" aria-hidden="true" tabindex="-1"></a>bracken_paths <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dirs, <span class="st">"bracken_counts.tsv"</span>)</span>
+<span id="cb51-354"><a href="#cb51-354" aria-hidden="true" tabindex="-1"></a>bracken <span class="ot">&lt;-</span> <span class="fu">lapply</span>(bracken_paths, read_tsv, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb51-355"><a href="#cb51-355" aria-hidden="true" tabindex="-1"></a>total_assigned <span class="ot">&lt;-</span> bracken <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">summarize</span>(</span>
+<span id="cb51-356"><a href="#cb51-356" aria-hidden="true" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">"Total"</span>,</span>
+<span id="cb51-357"><a href="#cb51-357" aria-hidden="true" tabindex="-1"></a>  <span class="at">kraken_assigned_reads =</span> <span class="fu">sum</span>(kraken_assigned_reads),</span>
+<span id="cb51-358"><a href="#cb51-358" aria-hidden="true" tabindex="-1"></a>  <span class="at">added_reads =</span> <span class="fu">sum</span>(added_reads),</span>
+<span id="cb51-359"><a href="#cb51-359" aria-hidden="true" tabindex="-1"></a>  <span class="at">new_est_reads =</span> <span class="fu">sum</span>(new_est_reads),</span>
+<span id="cb51-360"><a href="#cb51-360" aria-hidden="true" tabindex="-1"></a>  <span class="at">fraction_total_reads =</span> <span class="fu">sum</span>(fraction_total_reads)</span>
+<span id="cb51-361"><a href="#cb51-361" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb51-362"><a href="#cb51-362" aria-hidden="true" tabindex="-1"></a>bracken_spread <span class="ot">&lt;-</span> bracken <span class="sc">%&gt;%</span> <span class="fu">select</span>(name, sample, new_est_reads) <span class="sc">%&gt;%</span></span>
+<span id="cb51-363"><a href="#cb51-363" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">tolower</span>(name)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-364"><a href="#cb51-364" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">id_cols =</span> <span class="st">"sample"</span>, <span class="at">names_from =</span> <span class="st">"name"</span>, </span>
+<span id="cb51-365"><a href="#cb51-365" aria-hidden="true" tabindex="-1"></a>              <span class="at">values_from =</span> <span class="st">"new_est_reads"</span>)</span>
+<span id="cb51-366"><a href="#cb51-366" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-367"><a href="#cb51-367" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads</span></span>
+<span id="cb51-368"><a href="#cb51-368" aria-hidden="true" tabindex="-1"></a>read_counts_preproc <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> </span>
+<span id="cb51-369"><a href="#cb51-369" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, date, date_alias, city, stage, n_read_pairs) <span class="sc">%&gt;%</span></span>
+<span id="cb51-370"><a href="#cb51-370" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">id_cols =</span> <span class="fu">c</span>(<span class="st">"sample"</span>, <span class="st">"date"</span>, <span class="st">"date_alias"</span>, <span class="st">"city"</span>),</span>
+<span id="cb51-371"><a href="#cb51-371" aria-hidden="true" tabindex="-1"></a>              <span class="at">names_from=</span><span class="st">"stage"</span>, <span class="at">values_from=</span><span class="st">"n_read_pairs"</span>)</span>
+<span id="cb51-372"><a href="#cb51-372" aria-hidden="true" tabindex="-1"></a>read_counts <span class="ot">&lt;-</span> read_counts_preproc <span class="sc">%&gt;%</span></span>
+<span id="cb51-373"><a href="#cb51-373" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(total_assigned <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, new_est_reads), <span class="at">by =</span> <span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-374"><a href="#cb51-374" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">assigned =</span> new_est_reads) <span class="sc">%&gt;%</span></span>
+<span id="cb51-375"><a href="#cb51-375" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(bracken_spread, <span class="at">by=</span><span class="st">"sample"</span>)</span>
+<span id="cb51-376"><a href="#cb51-376" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-377"><a href="#cb51-377" aria-hidden="true" tabindex="-1"></a><span class="co"># Assess composition</span></span>
+<span id="cb51-378"><a href="#cb51-378" aria-hidden="true" tabindex="-1"></a>read_comp <span class="ot">&lt;-</span> <span class="fu">transmute</span>(read_counts, sample, date, date_alias, city,</span>
+<span id="cb51-379"><a href="#cb51-379" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_filtered =</span> raw_concat<span class="sc">-</span>cleaned,</span>
+<span id="cb51-380"><a href="#cb51-380" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_duplicate =</span> cleaned<span class="sc">-</span>dedup,</span>
+<span id="cb51-381"><a href="#cb51-381" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_ribosomal =</span> (dedup<span class="sc">-</span>ribo_initial) <span class="sc">+</span> (ribo_initial<span class="sc">-</span>ribo_secondary),</span>
+<span id="cb51-382"><a href="#cb51-382" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_unassigned =</span> ribo_secondary<span class="sc">-</span>assigned,</span>
+<span id="cb51-383"><a href="#cb51-383" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_bacterial =</span> bacteria,</span>
+<span id="cb51-384"><a href="#cb51-384" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_archaeal =</span> archaea,</span>
+<span id="cb51-385"><a href="#cb51-385" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_viral =</span> viruses,</span>
+<span id="cb51-386"><a href="#cb51-386" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_human =</span> eukaryota)</span>
+<span id="cb51-387"><a href="#cb51-387" aria-hidden="true" tabindex="-1"></a>read_comp_long <span class="ot">&lt;-</span> <span class="fu">pivot_longer</span>(read_comp, <span class="sc">-</span>(sample<span class="sc">:</span>city), </span>
+<span id="cb51-388"><a href="#cb51-388" aria-hidden="true" tabindex="-1"></a>                               <span class="at">names_to =</span> <span class="st">"classification"</span>,</span>
+<span id="cb51-389"><a href="#cb51-389" aria-hidden="true" tabindex="-1"></a>                               <span class="at">names_prefix =</span> <span class="st">"n_"</span>, <span class="at">values_to =</span> <span class="st">"n_reads"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-390"><a href="#cb51-390" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">classification =</span> <span class="fu">fct_inorder</span>(<span class="fu">str_to_sentence</span>(classification))) <span class="sc">%&gt;%</span></span>
+<span id="cb51-391"><a href="#cb51-391" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_reads<span class="sc">/</span><span class="fu">sum</span>(n_reads))</span>
+<span id="cb51-392"><a href="#cb51-392" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-393"><a href="#cb51-393" aria-hidden="true" tabindex="-1"></a><span class="co"># Summarize composition</span></span>
+<span id="cb51-394"><a href="#cb51-394" aria-hidden="true" tabindex="-1"></a>read_comp_summ <span class="ot">&lt;-</span> read_comp_long <span class="sc">%&gt;%</span> </span>
+<span id="cb51-395"><a href="#cb51-395" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(city, classification) <span class="sc">%&gt;%</span></span>
+<span id="cb51-396"><a href="#cb51-396" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads =</span> <span class="fu">sum</span>(n_reads), <span class="at">.groups =</span> <span class="st">"drop_last"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-397"><a href="#cb51-397" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_reads =</span> <span class="fu">replace_na</span>(n_reads,<span class="dv">0</span>),</span>
+<span id="cb51-398"><a href="#cb51-398" aria-hidden="true" tabindex="-1"></a>    <span class="at">p_reads =</span> n_reads<span class="sc">/</span><span class="fu">sum</span>(n_reads),</span>
+<span id="cb51-399"><a href="#cb51-399" aria-hidden="true" tabindex="-1"></a>    <span class="at">pc_reads =</span> p_reads<span class="sc">*</span><span class="dv">100</span>)</span>
+<span id="cb51-400"><a href="#cb51-400" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-401"><a href="#cb51-401" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-404"><a href="#cb51-404" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-405"><a href="#cb51-405" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-composition-all</span></span>
+<span id="cb51-406"><a href="#cb51-406" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 7</span></span>
+<span id="cb51-407"><a href="#cb51-407" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 8</span></span>
+<span id="cb51-408"><a href="#cb51-408" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-409"><a href="#cb51-409" aria-hidden="true" tabindex="-1"></a><span class="co"># Prepare plotting templates</span></span>
+<span id="cb51-410"><a href="#cb51-410" aria-hidden="true" tabindex="-1"></a>g_comp_base <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">mapping=</span><span class="fu">aes</span>(<span class="at">x=</span>date_alias, <span class="at">y=</span>p_reads, <span class="at">fill=</span>classification)) <span class="sc">+</span></span>
+<span id="cb51-411"><a href="#cb51-411" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_discrete</span>(<span class="at">name=</span><span class="st">"Collection Date"</span>) <span class="sc">+</span></span>
+<span id="cb51-412"><a href="#cb51-412" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>city, <span class="at">scales =</span> <span class="st">"free"</span>) <span class="sc">+</span></span>
+<span id="cb51-413"><a href="#cb51-413" aria-hidden="true" tabindex="-1"></a>  theme_kit <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_blank</span>())</span>
+<span id="cb51-414"><a href="#cb51-414" aria-hidden="true" tabindex="-1"></a>scale_y_pc_reads <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_y_continuous, <span class="at">name =</span> <span class="st">"% Reads"</span>,</span>
+<span id="cb51-415"><a href="#cb51-415" aria-hidden="true" tabindex="-1"></a>                                   <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>)</span>
+<span id="cb51-416"><a href="#cb51-416" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-417"><a href="#cb51-417" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot overall composition</span></span>
+<span id="cb51-418"><a href="#cb51-418" aria-hidden="true" tabindex="-1"></a>g_comp <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> <span class="fu">geom_col</span>(<span class="at">data =</span> read_comp_long, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-419"><a href="#cb51-419" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_pc_reads</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>)) <span class="sc">+</span></span>
+<span id="cb51-420"><a href="#cb51-420" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Classification"</span>)</span>
+<span id="cb51-421"><a href="#cb51-421" aria-hidden="true" tabindex="-1"></a>g_comp</span>
+<span id="cb51-422"><a href="#cb51-422" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-423"><a href="#cb51-423" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot composition of minor components</span></span>
+<span id="cb51-424"><a href="#cb51-424" aria-hidden="true" tabindex="-1"></a>read_comp_minor <span class="ot">&lt;-</span> read_comp_long <span class="sc">%&gt;%</span> </span>
+<span id="cb51-425"><a href="#cb51-425" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(classification <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"Archaeal"</span>, <span class="st">"Viral"</span>, <span class="st">"Other"</span>))</span>
+<span id="cb51-426"><a href="#cb51-426" aria-hidden="true" tabindex="-1"></a>palette_minor <span class="ot">&lt;-</span> <span class="fu">brewer.pal</span>(<span class="dv">9</span>, <span class="st">"Set1"</span>)[<span class="fu">c</span>(<span class="dv">6</span>,<span class="dv">7</span>,<span class="dv">9</span>)]</span>
+<span id="cb51-427"><a href="#cb51-427" aria-hidden="true" tabindex="-1"></a>g_comp_minor <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> <span class="fu">geom_col</span>(<span class="at">data=</span>read_comp_minor, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-428"><a href="#cb51-428" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_pc_reads</span>() <span class="sc">+</span></span>
+<span id="cb51-429"><a href="#cb51-429" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_minor, <span class="at">name =</span> <span class="st">"Classification"</span>)</span>
+<span id="cb51-430"><a href="#cb51-430" aria-hidden="true" tabindex="-1"></a>g_comp_minor</span>
+<span id="cb51-431"><a href="#cb51-431" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-432"><a href="#cb51-432" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-433"><a href="#cb51-433" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-436"><a href="#cb51-436" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-437"><a href="#cb51-437" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: composition-summary</span></span>
+<span id="cb51-438"><a href="#cb51-438" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-439"><a href="#cb51-439" aria-hidden="true" tabindex="-1"></a>p_reads_summ_group <span class="ot">&lt;-</span> read_comp_long <span class="sc">%&gt;%</span></span>
+<span id="cb51-440"><a href="#cb51-440" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">classification =</span> <span class="fu">ifelse</span>(classification <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"Filtered"</span>, <span class="st">"Duplicate"</span>, <span class="st">"Unassigned"</span>), <span class="st">"Excluded"</span>, <span class="fu">as.character</span>(classification)),</span>
+<span id="cb51-441"><a href="#cb51-441" aria-hidden="true" tabindex="-1"></a>         <span class="at">classification =</span> <span class="fu">fct_inorder</span>(classification)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-442"><a href="#cb51-442" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(classification, sample, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-443"><a href="#cb51-443" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads =</span> <span class="fu">sum</span>(p_reads), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-444"><a href="#cb51-444" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(classification, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-445"><a href="#cb51-445" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">pc_min =</span> <span class="fu">min</span>(p_reads)<span class="sc">*</span><span class="dv">100</span>, <span class="at">pc_max =</span> <span class="fu">max</span>(p_reads)<span class="sc">*</span><span class="dv">100</span>, </span>
+<span id="cb51-446"><a href="#cb51-446" aria-hidden="true" tabindex="-1"></a>            <span class="at">pc_mean =</span> <span class="fu">mean</span>(p_reads)<span class="sc">*</span><span class="dv">100</span>, <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb51-447"><a href="#cb51-447" aria-hidden="true" tabindex="-1"></a>p_reads_summ_prep <span class="ot">&lt;-</span> p_reads_summ_group <span class="sc">%&gt;%</span></span>
+<span id="cb51-448"><a href="#cb51-448" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">classification =</span> <span class="fu">fct_inorder</span>(classification),</span>
+<span id="cb51-449"><a href="#cb51-449" aria-hidden="true" tabindex="-1"></a>         <span class="at">pc_min =</span> pc_min <span class="sc">%&gt;%</span> <span class="fu">signif</span>(<span class="at">digits=</span><span class="dv">2</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(format, <span class="at">scientific=</span><span class="cn">FALSE</span>, <span class="at">trim=</span><span class="cn">TRUE</span>, <span class="at">digits=</span><span class="dv">2</span>),</span>
+<span id="cb51-450"><a href="#cb51-450" aria-hidden="true" tabindex="-1"></a>         <span class="at">pc_max =</span> pc_max <span class="sc">%&gt;%</span> <span class="fu">signif</span>(<span class="at">digits=</span><span class="dv">2</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(format, <span class="at">scientific=</span><span class="cn">FALSE</span>, <span class="at">trim=</span><span class="cn">TRUE</span>, <span class="at">digits=</span><span class="dv">2</span>),</span>
+<span id="cb51-451"><a href="#cb51-451" aria-hidden="true" tabindex="-1"></a>         <span class="at">pc_mean =</span> pc_mean <span class="sc">%&gt;%</span> <span class="fu">signif</span>(<span class="at">digits=</span><span class="dv">2</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(format, <span class="at">scientific=</span><span class="cn">FALSE</span>, <span class="at">trim=</span><span class="cn">TRUE</span>, <span class="at">digits=</span><span class="dv">2</span>),</span>
+<span id="cb51-452"><a href="#cb51-452" aria-hidden="true" tabindex="-1"></a>         <span class="at">display =</span> <span class="fu">paste0</span>(pc_min, <span class="st">"-"</span>, pc_max, <span class="st">"% (mean "</span>, pc_mean, <span class="st">"%)"</span>))</span>
+<span id="cb51-453"><a href="#cb51-453" aria-hidden="true" tabindex="-1"></a>p_reads_summ <span class="ot">&lt;-</span> p_reads_summ_prep <span class="sc">%&gt;%</span></span>
+<span id="cb51-454"><a href="#cb51-454" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(city, classification, <span class="at">read_fraction=</span>display) <span class="sc">%&gt;%</span></span>
+<span id="cb51-455"><a href="#cb51-455" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(city, classification)</span>
+<span id="cb51-456"><a href="#cb51-456" aria-hidden="true" tabindex="-1"></a>p_reads_summ</span>
+<span id="cb51-457"><a href="#cb51-457" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-458"><a href="#cb51-458" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-459"><a href="#cb51-459" aria-hidden="true" tabindex="-1"></a>In many respects, these resemble the Prussin data: high human fraction (mean 19.6%), high bacterial fraction (mean 18.2%), high unclassified fraction (mean 43.9%), low viral fraction (mean 0.01%). One notable difference is that archaeal reads are more abundant (0.034% compared to 0.016% for Prussin).</span>
+<span id="cb51-460"><a href="#cb51-460" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-461"><a href="#cb51-461" aria-hidden="true" tabindex="-1"></a>As in Prussin, viral DNA reads were dominated by *Caudoviricetes* phages. Other viral classes that are prominent in at least some samples include *Herviviricetes* (herpesviruses), *Papovaviricetes* (polyomaviruses and papillomaviruses), *Revtraviricetes* (retroviruses + Hep B), and *Naldaviricetes* (mainly arthropod viruses). I'll investigate the first three of this latter group in more depth, restricting in each case to samples where that family makes up at least 5% of viral reads.</span>
+<span id="cb51-462"><a href="#cb51-462" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-465"><a href="#cb51-465" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-466"><a href="#cb51-466" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: extract-viral-taxa</span></span>
+<span id="cb51-467"><a href="#cb51-467" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-468"><a href="#cb51-468" aria-hidden="true" tabindex="-1"></a><span class="co"># Get viral taxonomy</span></span>
+<span id="cb51-469"><a href="#cb51-469" aria-hidden="true" tabindex="-1"></a>viral_taxa_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir_base, <span class="st">"viral-taxids.tsv.gz"</span>)</span>
+<span id="cb51-470"><a href="#cb51-470" aria-hidden="true" tabindex="-1"></a>viral_taxa <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(viral_taxa_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb51-471"><a href="#cb51-471" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-472"><a href="#cb51-472" aria-hidden="true" tabindex="-1"></a><span class="co"># Get paths to Kraken reports</span></span>
+<span id="cb51-473"><a href="#cb51-473" aria-hidden="true" tabindex="-1"></a>samples <span class="ot">&lt;-</span> <span class="fu">as.character</span>(basic_stats_raw<span class="sc">$</span>sample)</span>
+<span id="cb51-474"><a href="#cb51-474" aria-hidden="true" tabindex="-1"></a>report_dirs <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dirs, <span class="st">"kraken"</span>)</span>
+<span id="cb51-475"><a href="#cb51-475" aria-hidden="true" tabindex="-1"></a>report_paths <span class="ot">&lt;-</span> <span class="fu">lapply</span>(report_dirs, list.files, <span class="at">full.names =</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> unlist</span>
+<span id="cb51-476"><a href="#cb51-476" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(report_paths) <span class="ot">&lt;-</span> <span class="fu">str_extract</span>(report_paths, <span class="st">"SRR</span><span class="sc">\\</span><span class="st">d*"</span>)</span>
+<span id="cb51-477"><a href="#cb51-477" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-478"><a href="#cb51-478" aria-hidden="true" tabindex="-1"></a><span class="co"># Extract viral taxa</span></span>
+<span id="cb51-479"><a href="#cb51-479" aria-hidden="true" tabindex="-1"></a>col_names <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"pc_reads_total"</span>, <span class="st">"n_reads_clade"</span>, <span class="st">"n_reads_direct"</span>,</span>
+<span id="cb51-480"><a href="#cb51-480" aria-hidden="true" tabindex="-1"></a>               <span class="st">"rank"</span>, <span class="st">"taxid"</span>, <span class="st">"name"</span>)</span>
+<span id="cb51-481"><a href="#cb51-481" aria-hidden="true" tabindex="-1"></a>kraken_reports_raw <span class="ot">&lt;-</span> <span class="fu">lapply</span>(report_paths, read_tsv, <span class="at">col_names =</span> col_names,</span>
+<span id="cb51-482"><a href="#cb51-482" aria-hidden="true" tabindex="-1"></a>                             <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb51-483"><a href="#cb51-483" aria-hidden="true" tabindex="-1"></a>kraken_reports <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="fu">names</span>(kraken_reports_raw), </span>
+<span id="cb51-484"><a href="#cb51-484" aria-hidden="true" tabindex="-1"></a>                         <span class="cf">function</span>(x) kraken_reports_raw[[x]] <span class="sc">%&gt;%</span> </span>
+<span id="cb51-485"><a href="#cb51-485" aria-hidden="true" tabindex="-1"></a>                           <span class="fu">mutate</span>(<span class="at">sample =</span> x)) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb51-486"><a href="#cb51-486" aria-hidden="true" tabindex="-1"></a>kraken_reports_viral <span class="ot">&lt;-</span> <span class="fu">filter</span>(kraken_reports, taxid <span class="sc">%in%</span> viral_taxa<span class="sc">$</span>taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-487"><a href="#cb51-487" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span></span>
+<span id="cb51-488"><a href="#cb51-488" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_viral =</span> n_reads_clade<span class="sc">/</span>n_reads_clade[<span class="dv">1</span>])</span>
+<span id="cb51-489"><a href="#cb51-489" aria-hidden="true" tabindex="-1"></a>kraken_reports_viral_cleaned <span class="ot">&lt;-</span> kraken_reports_viral <span class="sc">%&gt;%</span></span>
+<span id="cb51-490"><a href="#cb51-490" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-491"><a href="#cb51-491" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="sc">-</span>pc_reads_total, <span class="sc">-</span>n_reads_direct) <span class="sc">%&gt;%</span></span>
+<span id="cb51-492"><a href="#cb51-492" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, p_reads_viral, n_reads_clade, <span class="fu">everything</span>())</span>
+<span id="cb51-493"><a href="#cb51-493" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-494"><a href="#cb51-494" aria-hidden="true" tabindex="-1"></a>viral_classes <span class="ot">&lt;-</span> kraken_reports_viral_cleaned <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"C"</span>)</span>
+<span id="cb51-495"><a href="#cb51-495" aria-hidden="true" tabindex="-1"></a>viral_families <span class="ot">&lt;-</span> kraken_reports_viral_cleaned <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"F"</span>)</span>
+<span id="cb51-496"><a href="#cb51-496" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-497"><a href="#cb51-497" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-498"><a href="#cb51-498" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-501"><a href="#cb51-501" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-502"><a href="#cb51-502" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: viral-class-composition</span></span>
+<span id="cb51-503"><a href="#cb51-503" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-504"><a href="#cb51-504" aria-hidden="true" tabindex="-1"></a>major_threshold <span class="ot">&lt;-</span> <span class="fl">0.05</span></span>
+<span id="cb51-505"><a href="#cb51-505" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-506"><a href="#cb51-506" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify major viral classes</span></span>
+<span id="cb51-507"><a href="#cb51-507" aria-hidden="true" tabindex="-1"></a>viral_classes_major_tab <span class="ot">&lt;-</span> viral_classes <span class="sc">%&gt;%</span> </span>
+<span id="cb51-508"><a href="#cb51-508" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-509"><a href="#cb51-509" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_viral_max =</span> <span class="fu">max</span>(p_reads_viral), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-510"><a href="#cb51-510" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_viral_max <span class="sc">&gt;=</span> major_threshold)</span>
+<span id="cb51-511"><a href="#cb51-511" aria-hidden="true" tabindex="-1"></a>viral_classes_major_list <span class="ot">&lt;-</span> viral_classes_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb51-512"><a href="#cb51-512" aria-hidden="true" tabindex="-1"></a>viral_classes_major <span class="ot">&lt;-</span> viral_classes <span class="sc">%&gt;%</span> </span>
+<span id="cb51-513"><a href="#cb51-513" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">%in%</span> viral_classes_major_list) <span class="sc">%&gt;%</span></span>
+<span id="cb51-514"><a href="#cb51-514" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, date_alias, city, p_reads_viral)</span>
+<span id="cb51-515"><a href="#cb51-515" aria-hidden="true" tabindex="-1"></a>viral_classes_minor <span class="ot">&lt;-</span> viral_classes_major <span class="sc">%&gt;%</span> </span>
+<span id="cb51-516"><a href="#cb51-516" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-517"><a href="#cb51-517" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_viral_major =</span> <span class="fu">sum</span>(p_reads_viral), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-518"><a href="#cb51-518" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="st">"Other"</span>, <span class="at">taxid=</span><span class="cn">NA</span>, <span class="at">p_reads_viral =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_viral_major) <span class="sc">%&gt;%</span></span>
+<span id="cb51-519"><a href="#cb51-519" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, date_alias, city, p_reads_viral)</span>
+<span id="cb51-520"><a href="#cb51-520" aria-hidden="true" tabindex="-1"></a>viral_classes_display <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(viral_classes_major, viral_classes_minor) <span class="sc">%&gt;%</span></span>
+<span id="cb51-521"><a href="#cb51-521" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_viral)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-522"><a href="#cb51-522" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">factor</span>(name, <span class="at">levels=</span><span class="fu">c</span>(viral_classes_major_list, <span class="st">"Other"</span>)),</span>
+<span id="cb51-523"><a href="#cb51-523" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_viral =</span> <span class="fu">pmax</span>(p_reads_viral, <span class="dv">0</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-524"><a href="#cb51-524" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_viral, <span class="at">classification=</span>name)</span>
+<span id="cb51-525"><a href="#cb51-525" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-526"><a href="#cb51-526" aria-hidden="true" tabindex="-1"></a>palette_viral <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">brewer.pal</span>(<span class="dv">12</span>, <span class="st">"Set3"</span>), <span class="fu">brewer.pal</span>(<span class="dv">8</span>, <span class="st">"Dark2"</span>))</span>
+<span id="cb51-527"><a href="#cb51-527" aria-hidden="true" tabindex="-1"></a>g_classes <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-528"><a href="#cb51-528" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>viral_classes_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-529"><a href="#cb51-529" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Viral Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-530"><a href="#cb51-530" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-531"><a href="#cb51-531" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral class"</span>)</span>
+<span id="cb51-532"><a href="#cb51-532" aria-hidden="true" tabindex="-1"></a>  </span>
+<span id="cb51-533"><a href="#cb51-533" aria-hidden="true" tabindex="-1"></a>g_classes</span>
+<span id="cb51-534"><a href="#cb51-534" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-535"><a href="#cb51-535" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-536"><a href="#cb51-536" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-537"><a href="#cb51-537" aria-hidden="true" tabindex="-1"></a>*Papovaviricetes* are quite heterogeneous across samples, and frequently diverse within samples. *Alphapolyomavirus* and *Alphapapillomavirus* are the most abundant genera overall, but *Betapapillomavirus, Gammapapillomavirus, Mupapillomavirus* and others all have strong showings.</span>
+<span id="cb51-538"><a href="#cb51-538" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-541"><a href="#cb51-541" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-542"><a href="#cb51-542" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: papovaviricetes</span></span>
+<span id="cb51-543"><a href="#cb51-543" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-544"><a href="#cb51-544" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb51-545"><a href="#cb51-545" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-546"><a href="#cb51-546" aria-hidden="true" tabindex="-1"></a><span class="co"># Get samples</span></span>
+<span id="cb51-547"><a href="#cb51-547" aria-hidden="true" tabindex="-1"></a>papova_taxid <span class="ot">&lt;-</span> <span class="dv">2732421</span></span>
+<span id="cb51-548"><a href="#cb51-548" aria-hidden="true" tabindex="-1"></a>papova_threshold <span class="ot">&lt;-</span> <span class="fl">0.05</span></span>
+<span id="cb51-549"><a href="#cb51-549" aria-hidden="true" tabindex="-1"></a>papova_samples <span class="ot">&lt;-</span> viral_classes <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> papova_taxid) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_viral <span class="sc">&gt;</span> <span class="fl">0.05</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(sample) <span class="sc">%&gt;%</span> unique</span>
+<span id="cb51-550"><a href="#cb51-550" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-551"><a href="#cb51-551" aria-hidden="true" tabindex="-1"></a><span class="co"># Get all taxa in class</span></span>
+<span id="cb51-552"><a href="#cb51-552" aria-hidden="true" tabindex="-1"></a>papova_desc_taxids_old <span class="ot">&lt;-</span> papova_taxid</span>
+<span id="cb51-553"><a href="#cb51-553" aria-hidden="true" tabindex="-1"></a>papova_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(papova_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> papova_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb51-554"><a href="#cb51-554" aria-hidden="true" tabindex="-1"></a><span class="cf">while</span> (<span class="fu">length</span>(papova_desc_taxids_new) <span class="sc">&gt;</span> <span class="fu">length</span>(papova_desc_taxids_old)){</span>
+<span id="cb51-555"><a href="#cb51-555" aria-hidden="true" tabindex="-1"></a>  papova_desc_taxids_old <span class="ot">&lt;-</span> papova_desc_taxids_new</span>
+<span id="cb51-556"><a href="#cb51-556" aria-hidden="true" tabindex="-1"></a>  papova_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(papova_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> papova_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb51-557"><a href="#cb51-557" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-558"><a href="#cb51-558" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-559"><a href="#cb51-559" aria-hidden="true" tabindex="-1"></a><span class="co"># Get read counts</span></span>
+<span id="cb51-560"><a href="#cb51-560" aria-hidden="true" tabindex="-1"></a>papova_counts <span class="ot">&lt;-</span> kraken_reports_viral_cleaned <span class="sc">%&gt;%</span></span>
+<span id="cb51-561"><a href="#cb51-561" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">%in%</span> papova_desc_taxids_new,</span>
+<span id="cb51-562"><a href="#cb51-562" aria-hidden="true" tabindex="-1"></a>         sample <span class="sc">%in%</span> papova_samples) <span class="sc">%&gt;%</span></span>
+<span id="cb51-563"><a href="#cb51-563" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_papova =</span> n_reads_clade<span class="sc">/</span>n_reads_clade[<span class="dv">1</span>])</span>
+<span id="cb51-564"><a href="#cb51-564" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-565"><a href="#cb51-565" aria-hidden="true" tabindex="-1"></a><span class="co"># Get genus composition</span></span>
+<span id="cb51-566"><a href="#cb51-566" aria-hidden="true" tabindex="-1"></a>papova_genera <span class="ot">&lt;-</span> papova_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"G"</span>)</span>
+<span id="cb51-567"><a href="#cb51-567" aria-hidden="true" tabindex="-1"></a>papova_genera_major_tab <span class="ot">&lt;-</span> papova_genera <span class="sc">%&gt;%</span> </span>
+<span id="cb51-568"><a href="#cb51-568" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-569"><a href="#cb51-569" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_papova_max =</span> <span class="fu">max</span>(p_reads_papova), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-570"><a href="#cb51-570" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_papova_max <span class="sc">&gt;=</span> papova_threshold)</span>
+<span id="cb51-571"><a href="#cb51-571" aria-hidden="true" tabindex="-1"></a>papova_genera_major_list <span class="ot">&lt;-</span> papova_genera_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb51-572"><a href="#cb51-572" aria-hidden="true" tabindex="-1"></a>papova_genera_major <span class="ot">&lt;-</span> papova_genera <span class="sc">%&gt;%</span> </span>
+<span id="cb51-573"><a href="#cb51-573" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">%in%</span> papova_genera_major_list) <span class="sc">%&gt;%</span></span>
+<span id="cb51-574"><a href="#cb51-574" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, date_alias, city, p_reads_papova)</span>
+<span id="cb51-575"><a href="#cb51-575" aria-hidden="true" tabindex="-1"></a>papova_genera_minor <span class="ot">&lt;-</span> papova_genera_major <span class="sc">%&gt;%</span> </span>
+<span id="cb51-576"><a href="#cb51-576" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-577"><a href="#cb51-577" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_papova_major =</span> <span class="fu">sum</span>(p_reads_papova), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-578"><a href="#cb51-578" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="st">"Other"</span>, <span class="at">taxid=</span><span class="cn">NA</span>, <span class="at">p_reads_papova =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_papova_major) <span class="sc">%&gt;%</span></span>
+<span id="cb51-579"><a href="#cb51-579" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, date_alias, city, p_reads_papova)</span>
+<span id="cb51-580"><a href="#cb51-580" aria-hidden="true" tabindex="-1"></a>papova_genera_display <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(papova_genera_major, papova_genera_minor) <span class="sc">%&gt;%</span></span>
+<span id="cb51-581"><a href="#cb51-581" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_papova)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-582"><a href="#cb51-582" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">factor</span>(name, <span class="at">levels=</span><span class="fu">c</span>(papova_genera_major_list, <span class="st">"Other"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb51-583"><a href="#cb51-583" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_papova, <span class="at">classification=</span>name)</span>
+<span id="cb51-584"><a href="#cb51-584" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-585"><a href="#cb51-585" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-586"><a href="#cb51-586" aria-hidden="true" tabindex="-1"></a>g_papova_genera <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-587"><a href="#cb51-587" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>papova_genera_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-588"><a href="#cb51-588" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Papovaviricetes Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.02</span>), </span>
+<span id="cb51-589"><a href="#cb51-589" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-590"><a href="#cb51-590" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-591"><a href="#cb51-591" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral genus"</span>) <span class="sc">+</span></span>
+<span id="cb51-592"><a href="#cb51-592" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>))</span>
+<span id="cb51-593"><a href="#cb51-593" aria-hidden="true" tabindex="-1"></a>g_papova_genera</span>
+<span id="cb51-594"><a href="#cb51-594" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-595"><a href="#cb51-595" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-596"><a href="#cb51-596" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-597"><a href="#cb51-597" aria-hidden="true" tabindex="-1"></a>Only a few samples showed at least 5% prevalence of *Herviviricetes*, but those that did were typically dominated by one or a small number of species that varied between samples. Of these, human alphaherpesvirus 1 appeared in the most samples, but several other species were prominent in at least one sample:</span>
+<span id="cb51-598"><a href="#cb51-598" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-601"><a href="#cb51-601" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-602"><a href="#cb51-602" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: herviviricetes</span></span>
+<span id="cb51-603"><a href="#cb51-603" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-604"><a href="#cb51-604" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 9</span></span>
+<span id="cb51-605"><a href="#cb51-605" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-606"><a href="#cb51-606" aria-hidden="true" tabindex="-1"></a><span class="co"># Get samples</span></span>
+<span id="cb51-607"><a href="#cb51-607" aria-hidden="true" tabindex="-1"></a>hervi_taxid <span class="ot">&lt;-</span> <span class="dv">2731363</span></span>
+<span id="cb51-608"><a href="#cb51-608" aria-hidden="true" tabindex="-1"></a>hervi_threshold <span class="ot">&lt;-</span> <span class="fl">0.05</span></span>
+<span id="cb51-609"><a href="#cb51-609" aria-hidden="true" tabindex="-1"></a>hervi_samples <span class="ot">&lt;-</span> viral_classes <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> hervi_taxid) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_viral <span class="sc">&gt;</span> <span class="fl">0.05</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(sample) <span class="sc">%&gt;%</span> unique</span>
+<span id="cb51-610"><a href="#cb51-610" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-611"><a href="#cb51-611" aria-hidden="true" tabindex="-1"></a><span class="co"># Get all taxa in class</span></span>
+<span id="cb51-612"><a href="#cb51-612" aria-hidden="true" tabindex="-1"></a>hervi_desc_taxids_old <span class="ot">&lt;-</span> hervi_taxid</span>
+<span id="cb51-613"><a href="#cb51-613" aria-hidden="true" tabindex="-1"></a>hervi_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(hervi_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> hervi_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb51-614"><a href="#cb51-614" aria-hidden="true" tabindex="-1"></a><span class="cf">while</span> (<span class="fu">length</span>(hervi_desc_taxids_new) <span class="sc">&gt;</span> <span class="fu">length</span>(hervi_desc_taxids_old)){</span>
+<span id="cb51-615"><a href="#cb51-615" aria-hidden="true" tabindex="-1"></a>  hervi_desc_taxids_old <span class="ot">&lt;-</span> hervi_desc_taxids_new</span>
+<span id="cb51-616"><a href="#cb51-616" aria-hidden="true" tabindex="-1"></a>  hervi_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(hervi_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> hervi_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb51-617"><a href="#cb51-617" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-618"><a href="#cb51-618" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-619"><a href="#cb51-619" aria-hidden="true" tabindex="-1"></a><span class="co"># Get read counts</span></span>
+<span id="cb51-620"><a href="#cb51-620" aria-hidden="true" tabindex="-1"></a>hervi_counts <span class="ot">&lt;-</span> kraken_reports_viral_cleaned <span class="sc">%&gt;%</span></span>
+<span id="cb51-621"><a href="#cb51-621" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">%in%</span> hervi_desc_taxids_new,</span>
+<span id="cb51-622"><a href="#cb51-622" aria-hidden="true" tabindex="-1"></a>         sample <span class="sc">%in%</span> hervi_samples) <span class="sc">%&gt;%</span></span>
+<span id="cb51-623"><a href="#cb51-623" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hervi =</span> n_reads_clade<span class="sc">/</span>n_reads_clade[<span class="dv">1</span>])</span>
+<span id="cb51-624"><a href="#cb51-624" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-625"><a href="#cb51-625" aria-hidden="true" tabindex="-1"></a><span class="co"># Get genus composition</span></span>
+<span id="cb51-626"><a href="#cb51-626" aria-hidden="true" tabindex="-1"></a>hervi_genera <span class="ot">&lt;-</span> hervi_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"S"</span>)</span>
+<span id="cb51-627"><a href="#cb51-627" aria-hidden="true" tabindex="-1"></a>hervi_genera_major_tab <span class="ot">&lt;-</span> hervi_genera <span class="sc">%&gt;%</span> </span>
+<span id="cb51-628"><a href="#cb51-628" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-629"><a href="#cb51-629" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_hervi_max =</span> <span class="fu">max</span>(p_reads_hervi), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-630"><a href="#cb51-630" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hervi_max <span class="sc">&gt;=</span> hervi_threshold)</span>
+<span id="cb51-631"><a href="#cb51-631" aria-hidden="true" tabindex="-1"></a>hervi_genera_major_list <span class="ot">&lt;-</span> hervi_genera_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb51-632"><a href="#cb51-632" aria-hidden="true" tabindex="-1"></a>hervi_genera_major <span class="ot">&lt;-</span> hervi_genera <span class="sc">%&gt;%</span> </span>
+<span id="cb51-633"><a href="#cb51-633" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">%in%</span> hervi_genera_major_list) <span class="sc">%&gt;%</span></span>
+<span id="cb51-634"><a href="#cb51-634" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, date_alias, city, p_reads_hervi)</span>
+<span id="cb51-635"><a href="#cb51-635" aria-hidden="true" tabindex="-1"></a>hervi_genera_minor <span class="ot">&lt;-</span> hervi_genera_major <span class="sc">%&gt;%</span> </span>
+<span id="cb51-636"><a href="#cb51-636" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-637"><a href="#cb51-637" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_hervi_major =</span> <span class="fu">sum</span>(p_reads_hervi), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-638"><a href="#cb51-638" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="st">"Other"</span>, <span class="at">taxid=</span><span class="cn">NA</span>, <span class="at">p_reads_hervi =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_hervi_major) <span class="sc">%&gt;%</span></span>
+<span id="cb51-639"><a href="#cb51-639" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, date_alias, city, p_reads_hervi)</span>
+<span id="cb51-640"><a href="#cb51-640" aria-hidden="true" tabindex="-1"></a>hervi_genera_display <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(hervi_genera_major, hervi_genera_minor) <span class="sc">%&gt;%</span></span>
+<span id="cb51-641"><a href="#cb51-641" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hervi)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-642"><a href="#cb51-642" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">factor</span>(name, <span class="at">levels=</span><span class="fu">c</span>(hervi_genera_major_list, <span class="st">"Other"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb51-643"><a href="#cb51-643" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_hervi, <span class="at">classification=</span>name)</span>
+<span id="cb51-644"><a href="#cb51-644" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-645"><a href="#cb51-645" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-646"><a href="#cb51-646" aria-hidden="true" tabindex="-1"></a>g_hervi_genera <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-647"><a href="#cb51-647" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>hervi_genera_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-648"><a href="#cb51-648" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Herviviricetes Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb51-649"><a href="#cb51-649" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-650"><a href="#cb51-650" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-651"><a href="#cb51-651" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral genus"</span>) <span class="sc">+</span></span>
+<span id="cb51-652"><a href="#cb51-652" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>))</span>
+<span id="cb51-653"><a href="#cb51-653" aria-hidden="true" tabindex="-1"></a>g_hervi_genera</span>
+<span id="cb51-654"><a href="#cb51-654" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-655"><a href="#cb51-655" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-656"><a href="#cb51-656" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-657"><a href="#cb51-657" aria-hidden="true" tabindex="-1"></a>Finally, for *Revtraviricetes*, most samples were dominated by porcine type-C oncovirus, while one was dominated by an avian retrovirus. The last showed significant levels of two murine viruses plus HIV. I'm suspicious of many of these.</span>
+<span id="cb51-658"><a href="#cb51-658" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-661"><a href="#cb51-661" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-662"><a href="#cb51-662" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: revtraviricetes</span></span>
+<span id="cb51-663"><a href="#cb51-663" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-664"><a href="#cb51-664" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 9</span></span>
+<span id="cb51-665"><a href="#cb51-665" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-666"><a href="#cb51-666" aria-hidden="true" tabindex="-1"></a><span class="co"># Get samples</span></span>
+<span id="cb51-667"><a href="#cb51-667" aria-hidden="true" tabindex="-1"></a>revtra_taxid <span class="ot">&lt;-</span> <span class="dv">2732514</span></span>
+<span id="cb51-668"><a href="#cb51-668" aria-hidden="true" tabindex="-1"></a>revtra_threshold <span class="ot">&lt;-</span> <span class="fl">0.05</span></span>
+<span id="cb51-669"><a href="#cb51-669" aria-hidden="true" tabindex="-1"></a>revtra_samples <span class="ot">&lt;-</span> viral_classes <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> revtra_taxid) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_viral <span class="sc">&gt;</span> <span class="fl">0.05</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(sample) <span class="sc">%&gt;%</span> unique</span>
+<span id="cb51-670"><a href="#cb51-670" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-671"><a href="#cb51-671" aria-hidden="true" tabindex="-1"></a><span class="co"># Get all taxa in class</span></span>
+<span id="cb51-672"><a href="#cb51-672" aria-hidden="true" tabindex="-1"></a>revtra_desc_taxids_old <span class="ot">&lt;-</span> revtra_taxid</span>
+<span id="cb51-673"><a href="#cb51-673" aria-hidden="true" tabindex="-1"></a>revtra_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(revtra_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> revtra_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb51-674"><a href="#cb51-674" aria-hidden="true" tabindex="-1"></a><span class="cf">while</span> (<span class="fu">length</span>(revtra_desc_taxids_new) <span class="sc">&gt;</span> <span class="fu">length</span>(revtra_desc_taxids_old)){</span>
+<span id="cb51-675"><a href="#cb51-675" aria-hidden="true" tabindex="-1"></a>  revtra_desc_taxids_old <span class="ot">&lt;-</span> revtra_desc_taxids_new</span>
+<span id="cb51-676"><a href="#cb51-676" aria-hidden="true" tabindex="-1"></a>  revtra_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(revtra_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> revtra_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb51-677"><a href="#cb51-677" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-678"><a href="#cb51-678" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-679"><a href="#cb51-679" aria-hidden="true" tabindex="-1"></a><span class="co"># Get read counts</span></span>
+<span id="cb51-680"><a href="#cb51-680" aria-hidden="true" tabindex="-1"></a>revtra_counts <span class="ot">&lt;-</span> kraken_reports_viral_cleaned <span class="sc">%&gt;%</span></span>
+<span id="cb51-681"><a href="#cb51-681" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">%in%</span> revtra_desc_taxids_new,</span>
+<span id="cb51-682"><a href="#cb51-682" aria-hidden="true" tabindex="-1"></a>         sample <span class="sc">%in%</span> revtra_samples) <span class="sc">%&gt;%</span></span>
+<span id="cb51-683"><a href="#cb51-683" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_revtra =</span> n_reads_clade<span class="sc">/</span>n_reads_clade[<span class="dv">1</span>])</span>
+<span id="cb51-684"><a href="#cb51-684" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-685"><a href="#cb51-685" aria-hidden="true" tabindex="-1"></a><span class="co"># Get genus composition</span></span>
+<span id="cb51-686"><a href="#cb51-686" aria-hidden="true" tabindex="-1"></a>revtra_genera <span class="ot">&lt;-</span> revtra_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"S"</span>)</span>
+<span id="cb51-687"><a href="#cb51-687" aria-hidden="true" tabindex="-1"></a>revtra_genera_major_tab <span class="ot">&lt;-</span> revtra_genera <span class="sc">%&gt;%</span> </span>
+<span id="cb51-688"><a href="#cb51-688" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-689"><a href="#cb51-689" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_revtra_max =</span> <span class="fu">max</span>(p_reads_revtra), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-690"><a href="#cb51-690" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_revtra_max <span class="sc">&gt;=</span> revtra_threshold)</span>
+<span id="cb51-691"><a href="#cb51-691" aria-hidden="true" tabindex="-1"></a>revtra_genera_major_list <span class="ot">&lt;-</span> revtra_genera_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb51-692"><a href="#cb51-692" aria-hidden="true" tabindex="-1"></a>revtra_genera_major <span class="ot">&lt;-</span> revtra_genera <span class="sc">%&gt;%</span> </span>
+<span id="cb51-693"><a href="#cb51-693" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">%in%</span> revtra_genera_major_list) <span class="sc">%&gt;%</span></span>
+<span id="cb51-694"><a href="#cb51-694" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, date_alias, city, p_reads_revtra)</span>
+<span id="cb51-695"><a href="#cb51-695" aria-hidden="true" tabindex="-1"></a>revtra_genera_minor <span class="ot">&lt;-</span> revtra_genera_major <span class="sc">%&gt;%</span> </span>
+<span id="cb51-696"><a href="#cb51-696" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-697"><a href="#cb51-697" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_revtra_major =</span> <span class="fu">sum</span>(p_reads_revtra), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-698"><a href="#cb51-698" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="st">"Other"</span>, <span class="at">taxid=</span><span class="cn">NA</span>, <span class="at">p_reads_revtra =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_revtra_major) <span class="sc">%&gt;%</span></span>
+<span id="cb51-699"><a href="#cb51-699" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, date_alias, city, p_reads_revtra)</span>
+<span id="cb51-700"><a href="#cb51-700" aria-hidden="true" tabindex="-1"></a>revtra_genera_display <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(revtra_genera_major, revtra_genera_minor) <span class="sc">%&gt;%</span></span>
+<span id="cb51-701"><a href="#cb51-701" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_revtra)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-702"><a href="#cb51-702" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">factor</span>(name, <span class="at">levels=</span><span class="fu">c</span>(revtra_genera_major_list, <span class="st">"Other"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb51-703"><a href="#cb51-703" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_revtra, <span class="at">classification=</span>name)</span>
+<span id="cb51-704"><a href="#cb51-704" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-705"><a href="#cb51-705" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-706"><a href="#cb51-706" aria-hidden="true" tabindex="-1"></a>g_revtra_genera <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-707"><a href="#cb51-707" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>revtra_genera_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-708"><a href="#cb51-708" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% revtraviricetes Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb51-709"><a href="#cb51-709" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-710"><a href="#cb51-710" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-711"><a href="#cb51-711" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral genus"</span>) <span class="sc">+</span></span>
+<span id="cb51-712"><a href="#cb51-712" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>))</span>
+<span id="cb51-713"><a href="#cb51-713" aria-hidden="true" tabindex="-1"></a>g_revtra_genera</span>
+<span id="cb51-714"><a href="#cb51-714" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-715"><a href="#cb51-715" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-716"><a href="#cb51-716" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-717"><a href="#cb51-717" aria-hidden="true" tabindex="-1"></a><span class="fu"># Human-infecting virus reads: validation</span></span>
+<span id="cb51-718"><a href="#cb51-718" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-719"><a href="#cb51-719" aria-hidden="true" tabindex="-1"></a>Next, I investigated the human-infecting virus read content of these unenriched samples. Using the same workflow I used for Prussin et al, I identified 24,278 read pairs as putatively human viral: 0.002% of reads surviving to that stage in the pipeline.</span>
+<span id="cb51-720"><a href="#cb51-720" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-723"><a href="#cb51-723" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-724"><a href="#cb51-724" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-read-counts</span></span>
+<span id="cb51-725"><a href="#cb51-725" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-726"><a href="#cb51-726" aria-hidden="true" tabindex="-1"></a><span class="co"># Import HV read data</span></span>
+<span id="cb51-727"><a href="#cb51-727" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered_paths <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dirs, <span class="st">"hv_hits_putative_filtered.tsv.gz"</span>)</span>
+<span id="cb51-728"><a href="#cb51-728" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered <span class="ot">&lt;-</span> <span class="fu">lapply</span>(hv_reads_filtered_paths, read_tsv,</span>
+<span id="cb51-729"><a href="#cb51-729" aria-hidden="true" tabindex="-1"></a>                            <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-730"><a href="#cb51-730" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bind_rows</span>() <span class="sc">%&gt;%</span></span>
+<span id="cb51-731"><a href="#cb51-731" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>)</span>
+<span id="cb51-732"><a href="#cb51-732" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-733"><a href="#cb51-733" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads</span></span>
+<span id="cb51-734"><a href="#cb51-734" aria-hidden="true" tabindex="-1"></a>n_hv_filtered <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span></span>
+<span id="cb51-735"><a href="#cb51-735" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, date_alias, city, seq_id) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span></span>
+<span id="cb51-736"><a href="#cb51-736" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, date_alias, city) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> </span>
+<span id="cb51-737"><a href="#cb51-737" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(basic_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"ribo_initial"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-738"><a href="#cb51-738" aria-hidden="true" tabindex="-1"></a>               <span class="fu">select</span>(sample, n_read_pairs), <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-739"><a href="#cb51-739" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">n_putative =</span> n, <span class="at">n_total =</span> n_read_pairs) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-740"><a href="#cb51-740" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads <span class="sc">*</span> <span class="dv">100</span>)</span>
+<span id="cb51-741"><a href="#cb51-741" aria-hidden="true" tabindex="-1"></a>n_hv_filtered_summ <span class="ot">&lt;-</span> n_hv_filtered <span class="sc">%&gt;%</span> ungroup <span class="sc">%&gt;%</span></span>
+<span id="cb51-742"><a href="#cb51-742" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_putative =</span> <span class="fu">sum</span>(n_putative), <span class="at">n_total =</span> <span class="fu">sum</span>(n_total), </span>
+<span id="cb51-743"><a href="#cb51-743" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-744"><a href="#cb51-744" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads<span class="sc">*</span><span class="dv">100</span>)</span>
+<span id="cb51-745"><a href="#cb51-745" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-746"><a href="#cb51-746" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-749"><a href="#cb51-749" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-750"><a href="#cb51-750" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-hv-scores</span></span>
+<span id="cb51-751"><a href="#cb51-751" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb51-752"><a href="#cb51-752" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 8</span></span>
+<span id="cb51-753"><a href="#cb51-753" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-754"><a href="#cb51-754" aria-hidden="true" tabindex="-1"></a><span class="co"># Collapse multi-entry sequences</span></span>
+<span id="cb51-755"><a href="#cb51-755" aria-hidden="true" tabindex="-1"></a>rmax <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(max, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb51-756"><a href="#cb51-756" aria-hidden="true" tabindex="-1"></a>collapse <span class="ot">&lt;-</span> <span class="cf">function</span>(x) <span class="fu">ifelse</span>(<span class="fu">all</span>(x <span class="sc">==</span> x[<span class="dv">1</span>]), x[<span class="dv">1</span>], <span class="fu">paste</span>(x, <span class="at">collapse=</span><span class="st">"/"</span>))</span>
+<span id="cb51-757"><a href="#cb51-757" aria-hidden="true" tabindex="-1"></a>mrg <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span> </span>
+<span id="cb51-758"><a href="#cb51-758" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">adj_score_max =</span> <span class="fu">pmax</span>(adj_score_fwd, adj_score_rev, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-759"><a href="#cb51-759" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(adj_score_max)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-760"><a href="#cb51-760" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id) <span class="sc">%&gt;%</span></span>
+<span id="cb51-761"><a href="#cb51-761" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">sample =</span> <span class="fu">collapse</span>(sample),</span>
+<span id="cb51-762"><a href="#cb51-762" aria-hidden="true" tabindex="-1"></a>            <span class="at">genome_id =</span> <span class="fu">collapse</span>(genome_id),</span>
+<span id="cb51-763"><a href="#cb51-763" aria-hidden="true" tabindex="-1"></a>            <span class="at">taxid_best =</span> taxid[<span class="dv">1</span>],</span>
+<span id="cb51-764"><a href="#cb51-764" aria-hidden="true" tabindex="-1"></a>            <span class="at">taxid =</span> <span class="fu">collapse</span>(<span class="fu">as.character</span>(taxid)),</span>
+<span id="cb51-765"><a href="#cb51-765" aria-hidden="true" tabindex="-1"></a>            <span class="at">best_alignment_score_fwd =</span> <span class="fu">rmax</span>(best_alignment_score_fwd),</span>
+<span id="cb51-766"><a href="#cb51-766" aria-hidden="true" tabindex="-1"></a>            <span class="at">best_alignment_score_rev =</span> <span class="fu">rmax</span>(best_alignment_score_rev),</span>
+<span id="cb51-767"><a href="#cb51-767" aria-hidden="true" tabindex="-1"></a>            <span class="at">query_len_fwd =</span> <span class="fu">rmax</span>(query_len_fwd),</span>
+<span id="cb51-768"><a href="#cb51-768" aria-hidden="true" tabindex="-1"></a>            <span class="at">query_len_rev =</span> <span class="fu">rmax</span>(query_len_rev),</span>
+<span id="cb51-769"><a href="#cb51-769" aria-hidden="true" tabindex="-1"></a>            <span class="at">query_seq_fwd =</span> query_seq_fwd[<span class="sc">!</span><span class="fu">is.na</span>(query_seq_fwd)][<span class="dv">1</span>],</span>
+<span id="cb51-770"><a href="#cb51-770" aria-hidden="true" tabindex="-1"></a>            <span class="at">query_seq_rev =</span> query_seq_rev[<span class="sc">!</span><span class="fu">is.na</span>(query_seq_rev)][<span class="dv">1</span>],</span>
+<span id="cb51-771"><a href="#cb51-771" aria-hidden="true" tabindex="-1"></a>            <span class="at">classified =</span> <span class="fu">rmax</span>(classified),</span>
+<span id="cb51-772"><a href="#cb51-772" aria-hidden="true" tabindex="-1"></a>            <span class="at">assigned_name =</span> <span class="fu">collapse</span>(assigned_name),</span>
+<span id="cb51-773"><a href="#cb51-773" aria-hidden="true" tabindex="-1"></a>            <span class="at">assigned_taxid_best =</span> assigned_taxid[<span class="dv">1</span>],</span>
+<span id="cb51-774"><a href="#cb51-774" aria-hidden="true" tabindex="-1"></a>            <span class="at">assigned_taxid =</span> <span class="fu">collapse</span>(<span class="fu">as.character</span>(assigned_taxid)),</span>
+<span id="cb51-775"><a href="#cb51-775" aria-hidden="true" tabindex="-1"></a>            <span class="at">assigned_hv =</span> <span class="fu">rmax</span>(assigned_hv),</span>
+<span id="cb51-776"><a href="#cb51-776" aria-hidden="true" tabindex="-1"></a>            <span class="at">hit_hv =</span> <span class="fu">rmax</span>(hit_hv),</span>
+<span id="cb51-777"><a href="#cb51-777" aria-hidden="true" tabindex="-1"></a>            <span class="at">encoded_hits =</span> <span class="fu">collapse</span>(encoded_hits),</span>
+<span id="cb51-778"><a href="#cb51-778" aria-hidden="true" tabindex="-1"></a>            <span class="at">adj_score_fwd =</span> <span class="fu">rmax</span>(adj_score_fwd),</span>
+<span id="cb51-779"><a href="#cb51-779" aria-hidden="true" tabindex="-1"></a>            <span class="at">adj_score_rev =</span> <span class="fu">rmax</span>(adj_score_rev)</span>
+<span id="cb51-780"><a href="#cb51-780" aria-hidden="true" tabindex="-1"></a>            ) <span class="sc">%&gt;%</span></span>
+<span id="cb51-781"><a href="#cb51-781" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-782"><a href="#cb51-782" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">kraken_label =</span> <span class="fu">ifelse</span>(assigned_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">assignment"</span>,</span>
+<span id="cb51-783"><a href="#cb51-783" aria-hidden="true" tabindex="-1"></a>                               <span class="fu">ifelse</span>(hit_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">hit"</span>,</span>
+<span id="cb51-784"><a href="#cb51-784" aria-hidden="true" tabindex="-1"></a>                                      <span class="st">"No hit or</span><span class="sc">\n</span><span class="st">assignment"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb51-785"><a href="#cb51-785" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">adj_score_max =</span> <span class="fu">pmax</span>(adj_score_fwd, adj_score_rev),</span>
+<span id="cb51-786"><a href="#cb51-786" aria-hidden="true" tabindex="-1"></a>         <span class="at">highscore =</span> adj_score_max <span class="sc">&gt;=</span> <span class="dv">20</span>)</span>
+<span id="cb51-787"><a href="#cb51-787" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-788"><a href="#cb51-788" aria-hidden="true" tabindex="-1"></a>g_hist_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max)) <span class="sc">+</span> </span>
+<span id="cb51-789"><a href="#cb51-789" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>) <span class="sc">+</span></span>
+<span id="cb51-790"><a href="#cb51-790" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-791"><a href="#cb51-791" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>)), <span class="at">scales =</span> <span class="st">"free_y"</span>) <span class="sc">+</span></span>
+<span id="cb51-792"><a href="#cb51-792" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>) <span class="sc">+</span> </span>
+<span id="cb51-793"><a href="#cb51-793" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>) <span class="sc">+</span> </span>
+<span id="cb51-794"><a href="#cb51-794" aria-hidden="true" tabindex="-1"></a>  theme_base </span>
+<span id="cb51-795"><a href="#cb51-795" aria-hidden="true" tabindex="-1"></a>g_hist_0</span>
+<span id="cb51-796"><a href="#cb51-796" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-797"><a href="#cb51-797" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-798"><a href="#cb51-798" aria-hidden="true" tabindex="-1"></a>As previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I've used for previous datasets.</span>
+<span id="cb51-799"><a href="#cb51-799" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-802"><a href="#cb51-802" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-803"><a href="#cb51-803" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: make-blast-fasta</span></span>
+<span id="cb51-804"><a href="#cb51-804" aria-hidden="true" tabindex="-1"></a>mrg_fasta <span class="ot">&lt;-</span>  mrg <span class="sc">%&gt;%</span></span>
+<span id="cb51-805"><a href="#cb51-805" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">seq_head =</span> <span class="fu">paste0</span>(<span class="st">"&gt;"</span>, seq_id)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-806"><a href="#cb51-806" aria-hidden="true" tabindex="-1"></a>  ungroup <span class="sc">%&gt;%</span></span>
+<span id="cb51-807"><a href="#cb51-807" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">header1=</span>seq_head, <span class="at">seq1=</span>query_seq_fwd, </span>
+<span id="cb51-808"><a href="#cb51-808" aria-hidden="true" tabindex="-1"></a>         <span class="at">header2=</span>seq_head, <span class="at">seq2=</span>query_seq_rev) <span class="sc">%&gt;%</span></span>
+<span id="cb51-809"><a href="#cb51-809" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">header1=</span><span class="fu">paste0</span>(header1, <span class="st">"_1"</span>), <span class="at">header2=</span><span class="fu">paste0</span>(header2, <span class="st">"_2"</span>))</span>
+<span id="cb51-810"><a href="#cb51-810" aria-hidden="true" tabindex="-1"></a>mrg_fasta_sep <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(<span class="fu">select</span>(mrg_fasta, <span class="at">header=</span>header1, <span class="at">seq=</span>seq1),</span>
+<span id="cb51-811"><a href="#cb51-811" aria-hidden="true" tabindex="-1"></a>                           <span class="fu">select</span>(mrg_fasta, <span class="at">header=</span>header2, <span class="at">seq=</span>seq2)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-812"><a href="#cb51-812" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(seq))</span>
+<span id="cb51-813"><a href="#cb51-813" aria-hidden="true" tabindex="-1"></a>mrg_fasta_out <span class="ot">&lt;-</span> <span class="fu">do.call</span>(paste, <span class="fu">c</span>(mrg_fasta_sep, <span class="at">sep=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-814"><a href="#cb51-814" aria-hidden="true" tabindex="-1"></a>  <span class="fu">paste</span>(<span class="at">collapse=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span>
+<span id="cb51-815"><a href="#cb51-815" aria-hidden="true" tabindex="-1"></a>blast_dir <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir_base, <span class="st">"blast"</span>)</span>
+<span id="cb51-816"><a href="#cb51-816" aria-hidden="true" tabindex="-1"></a><span class="fu">dir.create</span>(blast_dir, <span class="at">showWarnings =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb51-817"><a href="#cb51-817" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(mrg_fasta_out, <span class="fu">file.path</span>(blast_dir, <span class="st">"putative-viral.fasta"</span>))</span>
+<span id="cb51-818"><a href="#cb51-818" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-819"><a href="#cb51-819" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-822"><a href="#cb51-822" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-823"><a href="#cb51-823" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: process-blast-data</span></span>
+<span id="cb51-824"><a href="#cb51-824" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb51-825"><a href="#cb51-825" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-826"><a href="#cb51-826" aria-hidden="true" tabindex="-1"></a><span class="co"># Import BLAST results</span></span>
+<span id="cb51-827"><a href="#cb51-827" aria-hidden="true" tabindex="-1"></a><span class="co"># blast_results_path &lt;- file.path(data_dir_base, "blast/putative-viral.blast.gz")</span></span>
+<span id="cb51-828"><a href="#cb51-828" aria-hidden="true" tabindex="-1"></a><span class="co"># blast_cols &lt;- c("qseqid", "sseqid", "sgi", "staxid", "qlen", "evalue", "bitscore", "qcovs", "length", "pident", "mismatch", "gapopen", "sstrand", "qstart", "qend", "sstart", "send")</span></span>
+<span id="cb51-829"><a href="#cb51-829" aria-hidden="true" tabindex="-1"></a><span class="co"># blast_results &lt;- read_tsv(blast_results_path, show_col_types = FALSE,</span></span>
+<span id="cb51-830"><a href="#cb51-830" aria-hidden="true" tabindex="-1"></a><span class="co">#                           col_names = blast_cols, col_types = cols(.default="c"))</span></span>
+<span id="cb51-831"><a href="#cb51-831" aria-hidden="true" tabindex="-1"></a>blast_results_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir_base, <span class="st">"blast/putative-viral-best.blast.gz"</span>)</span>
+<span id="cb51-832"><a href="#cb51-832" aria-hidden="true" tabindex="-1"></a>blast_results <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(blast_results_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb51-833"><a href="#cb51-833" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-834"><a href="#cb51-834" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter for best hit for each query/subject combination</span></span>
+<span id="cb51-835"><a href="#cb51-835" aria-hidden="true" tabindex="-1"></a>blast_results_best <span class="ot">&lt;-</span> blast_results <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(qseqid, staxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-836"><a href="#cb51-836" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(bitscore <span class="sc">==</span> <span class="fu">max</span>(bitscore)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-837"><a href="#cb51-837" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(length <span class="sc">==</span> <span class="fu">max</span>(length)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>)</span>
+<span id="cb51-838"><a href="#cb51-838" aria-hidden="true" tabindex="-1"></a><span class="fu">write_tsv</span>(blast_results_best, <span class="fu">file.path</span>(data_dir_base, <span class="st">"blast/putative-viral-best.blast.gz"</span>))</span>
+<span id="cb51-839"><a href="#cb51-839" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-840"><a href="#cb51-840" aria-hidden="true" tabindex="-1"></a><span class="co"># Rank hits for each query and filter for high-ranking hits</span></span>
+<span id="cb51-841"><a href="#cb51-841" aria-hidden="true" tabindex="-1"></a>blast_results_ranked <span class="ot">&lt;-</span> blast_results_best <span class="sc">%&gt;%</span> </span>
+<span id="cb51-842"><a href="#cb51-842" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(qseqid) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">rank =</span> <span class="fu">dense_rank</span>(<span class="fu">desc</span>(bitscore)))</span>
+<span id="cb51-843"><a href="#cb51-843" aria-hidden="true" tabindex="-1"></a>blast_results_highrank <span class="ot">&lt;-</span> blast_results_ranked <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">&lt;=</span> <span class="dv">5</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-844"><a href="#cb51-844" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">read_pair =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="sc">-</span><span class="dv">1</span>), </span>
+<span id="cb51-845"><a href="#cb51-845" aria-hidden="true" tabindex="-1"></a>         <span class="at">seq_id =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="dv">1</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-846"><a href="#cb51-846" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">bitscore =</span> <span class="fu">as.numeric</span>(bitscore))</span>
+<span id="cb51-847"><a href="#cb51-847" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-848"><a href="#cb51-848" aria-hidden="true" tabindex="-1"></a><span class="co"># Summarize by read pair and taxid</span></span>
+<span id="cb51-849"><a href="#cb51-849" aria-hidden="true" tabindex="-1"></a>blast_results_paired <span class="ot">&lt;-</span> blast_results_highrank <span class="sc">%&gt;%</span></span>
+<span id="cb51-850"><a href="#cb51-850" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id, staxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-851"><a href="#cb51-851" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">bitscore_max =</span> <span class="fu">max</span>(bitscore), <span class="at">bitscore_min =</span> <span class="fu">min</span>(bitscore),</span>
+<span id="cb51-852"><a href="#cb51-852" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads =</span> <span class="fu">n</span>(), <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb51-853"><a href="#cb51-853" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-854"><a href="#cb51-854" aria-hidden="true" tabindex="-1"></a><span class="co"># Add viral status</span></span>
+<span id="cb51-855"><a href="#cb51-855" aria-hidden="true" tabindex="-1"></a>blast_results_viral <span class="ot">&lt;-</span> <span class="fu">mutate</span>(blast_results_paired, <span class="at">viral =</span> staxid <span class="sc">%in%</span> viral_taxa<span class="sc">$</span>taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-856"><a href="#cb51-856" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_full =</span> viral <span class="sc">&amp;</span> n_reads <span class="sc">==</span> <span class="dv">2</span>)</span>
+<span id="cb51-857"><a href="#cb51-857" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-858"><a href="#cb51-858" aria-hidden="true" tabindex="-1"></a><span class="co"># Compare to Kraken &amp; Bowtie assignments</span></span>
+<span id="cb51-859"><a href="#cb51-859" aria-hidden="true" tabindex="-1"></a>match_taxid <span class="ot">&lt;-</span> <span class="cf">function</span>(taxid_1, taxid_2){</span>
+<span id="cb51-860"><a href="#cb51-860" aria-hidden="true" tabindex="-1"></a>  p1 <span class="ot">&lt;-</span> <span class="fu">mapply</span>(grepl, <span class="fu">paste0</span>(<span class="st">"/"</span>, taxid_1, <span class="st">"$"</span>), taxid_2)</span>
+<span id="cb51-861"><a href="#cb51-861" aria-hidden="true" tabindex="-1"></a>  p2 <span class="ot">&lt;-</span> <span class="fu">mapply</span>(grepl, <span class="fu">paste0</span>(<span class="st">"^"</span>, taxid_1, <span class="st">"/"</span>), taxid_2)</span>
+<span id="cb51-862"><a href="#cb51-862" aria-hidden="true" tabindex="-1"></a>  p3 <span class="ot">&lt;-</span> <span class="fu">mapply</span>(grepl, <span class="fu">paste0</span>(<span class="st">"^"</span>, taxid_1, <span class="st">"$"</span>), taxid_2)</span>
+<span id="cb51-863"><a href="#cb51-863" aria-hidden="true" tabindex="-1"></a>  out <span class="ot">&lt;-</span> <span class="fu">setNames</span>(p1<span class="sc">|</span>p2<span class="sc">|</span>p3, <span class="cn">NULL</span>)</span>
+<span id="cb51-864"><a href="#cb51-864" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(out)</span>
+<span id="cb51-865"><a href="#cb51-865" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-866"><a href="#cb51-866" aria-hidden="true" tabindex="-1"></a>mrg_assign <span class="ot">&lt;-</span> mrg <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, seq_id, taxid, assigned_taxid, adj_score_max)</span>
+<span id="cb51-867"><a href="#cb51-867" aria-hidden="true" tabindex="-1"></a>blast_results_assign <span class="ot">&lt;-</span> <span class="fu">inner_join</span>(blast_results_viral, mrg_assign, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-868"><a href="#cb51-868" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">taxid_match_bowtie =</span> <span class="fu">match_taxid</span>(staxid, taxid),</span>
+<span id="cb51-869"><a href="#cb51-869" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_kraken =</span> <span class="fu">match_taxid</span>(staxid, assigned_taxid),</span>
+<span id="cb51-870"><a href="#cb51-870" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_any =</span> taxid_match_bowtie <span class="sc">|</span> taxid_match_kraken)</span>
+<span id="cb51-871"><a href="#cb51-871" aria-hidden="true" tabindex="-1"></a>blast_results_out <span class="ot">&lt;-</span> blast_results_assign <span class="sc">%&gt;%</span></span>
+<span id="cb51-872"><a href="#cb51-872" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id) <span class="sc">%&gt;%</span></span>
+<span id="cb51-873"><a href="#cb51-873" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">viral_status =</span> <span class="fu">ifelse</span>(<span class="fu">any</span>(viral_full), <span class="dv">2</span>,</span>
+<span id="cb51-874"><a href="#cb51-874" aria-hidden="true" tabindex="-1"></a>                                  <span class="fu">ifelse</span>(<span class="fu">any</span>(taxid_match_any), <span class="dv">2</span>,</span>
+<span id="cb51-875"><a href="#cb51-875" aria-hidden="true" tabindex="-1"></a>                                             <span class="fu">ifelse</span>(<span class="fu">any</span>(viral), <span class="dv">1</span>, <span class="dv">0</span>))),</span>
+<span id="cb51-876"><a href="#cb51-876" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb51-877"><a href="#cb51-877" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-878"><a href="#cb51-878" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-881"><a href="#cb51-881" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-882"><a href="#cb51-882" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-blast-results</span></span>
+<span id="cb51-883"><a href="#cb51-883" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-884"><a href="#cb51-884" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb51-885"><a href="#cb51-885" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-886"><a href="#cb51-886" aria-hidden="true" tabindex="-1"></a><span class="co"># Merge BLAST results with unenriched read data</span></span>
+<span id="cb51-887"><a href="#cb51-887" aria-hidden="true" tabindex="-1"></a>mrg_blast <span class="ot">&lt;-</span> <span class="fu">full_join</span>(mrg, blast_results_out, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-888"><a href="#cb51-888" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_status =</span> <span class="fu">replace_na</span>(viral_status, <span class="dv">0</span>),</span>
+<span id="cb51-889"><a href="#cb51-889" aria-hidden="true" tabindex="-1"></a>         <span class="at">viral_status_out =</span> <span class="fu">ifelse</span>(viral_status <span class="sc">==</span> <span class="dv">0</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>))</span>
+<span id="cb51-890"><a href="#cb51-890" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-891"><a href="#cb51-891" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot RNA</span></span>
+<span id="cb51-892"><a href="#cb51-892" aria-hidden="true" tabindex="-1"></a>g_hist_1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg_blast, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max, <span class="at">fill=</span>viral_status_out)) <span class="sc">+</span> </span>
+<span id="cb51-893"><a href="#cb51-893" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>) <span class="sc">+</span> </span>
+<span id="cb51-894"><a href="#cb51-894" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb51-895"><a href="#cb51-895" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>)), <span class="at">scales =</span> <span class="st">"free_y"</span>) <span class="sc">+</span></span>
+<span id="cb51-896"><a href="#cb51-896" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>) <span class="sc">+</span> </span>
+<span id="cb51-897"><a href="#cb51-897" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>) <span class="sc">+</span> </span>
+<span id="cb51-898"><a href="#cb51-898" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Viral status"</span>) <span class="sc">+</span></span>
+<span id="cb51-899"><a href="#cb51-899" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb51-900"><a href="#cb51-900" aria-hidden="true" tabindex="-1"></a>g_hist_1</span>
+<span id="cb51-901"><a href="#cb51-901" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-902"><a href="#cb51-902" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-903"><a href="#cb51-903" aria-hidden="true" tabindex="-1"></a>For a disjunctive score threshold of 20, the workflow achieves a measured F1 score of 98.0%.</span>
+<span id="cb51-904"><a href="#cb51-904" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-907"><a href="#cb51-907" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-908"><a href="#cb51-908" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-f1</span></span>
+<span id="cb51-909"><a href="#cb51-909" aria-hidden="true" tabindex="-1"></a>test_sens_spec <span class="ot">&lt;-</span> <span class="cf">function</span>(tab, score_threshold){</span>
+<span id="cb51-910"><a href="#cb51-910" aria-hidden="true" tabindex="-1"></a>  tab_retained <span class="ot">&lt;-</span> tab <span class="sc">%&gt;%</span> </span>
+<span id="cb51-911"><a href="#cb51-911" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">retain_score =</span> (adj_score_fwd <span class="sc">&gt;</span> score_threshold <span class="sc">|</span> adj_score_rev <span class="sc">&gt;</span> score_threshold),</span>
+<span id="cb51-912"><a href="#cb51-912" aria-hidden="true" tabindex="-1"></a>           <span class="at">retain =</span> assigned_hv <span class="sc">|</span> hit_hv <span class="sc">|</span> retain_score) <span class="sc">%&gt;%</span></span>
+<span id="cb51-913"><a href="#cb51-913" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>(viral_status_out, retain) <span class="sc">%&gt;%</span> count</span>
+<span id="cb51-914"><a href="#cb51-914" aria-hidden="true" tabindex="-1"></a>  pos_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb51-915"><a href="#cb51-915" aria-hidden="true" tabindex="-1"></a>  pos_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb51-916"><a href="#cb51-916" aria-hidden="true" tabindex="-1"></a>  neg_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb51-917"><a href="#cb51-917" aria-hidden="true" tabindex="-1"></a>  neg_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb51-918"><a href="#cb51-918" aria-hidden="true" tabindex="-1"></a>  sensitivity <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> neg_fls)</span>
+<span id="cb51-919"><a href="#cb51-919" aria-hidden="true" tabindex="-1"></a>  specificity <span class="ot">&lt;-</span> neg_tru <span class="sc">/</span> (neg_tru <span class="sc">+</span> pos_fls)</span>
+<span id="cb51-920"><a href="#cb51-920" aria-hidden="true" tabindex="-1"></a>  precision   <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> pos_fls)</span>
+<span id="cb51-921"><a href="#cb51-921" aria-hidden="true" tabindex="-1"></a>  f1 <span class="ot">&lt;-</span> <span class="dv">2</span> <span class="sc">*</span> precision <span class="sc">*</span> sensitivity <span class="sc">/</span> (precision <span class="sc">+</span> sensitivity)</span>
+<span id="cb51-922"><a href="#cb51-922" aria-hidden="true" tabindex="-1"></a>  out <span class="ot">&lt;-</span> <span class="fu">tibble</span>(<span class="at">threshold=</span>score_threshold, <span class="at">sensitivity=</span>sensitivity, </span>
+<span id="cb51-923"><a href="#cb51-923" aria-hidden="true" tabindex="-1"></a>                <span class="at">specificity=</span>specificity, <span class="at">precision=</span>precision, <span class="at">f1=</span>f1)</span>
+<span id="cb51-924"><a href="#cb51-924" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(out)</span>
+<span id="cb51-925"><a href="#cb51-925" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-926"><a href="#cb51-926" aria-hidden="true" tabindex="-1"></a>range_f1 <span class="ot">&lt;-</span> <span class="cf">function</span>(intab, <span class="at">inrange=</span><span class="dv">15</span><span class="sc">:</span><span class="dv">45</span>){</span>
+<span id="cb51-927"><a href="#cb51-927" aria-hidden="true" tabindex="-1"></a>  tss <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(test_sens_spec, <span class="at">tab=</span>intab)</span>
+<span id="cb51-928"><a href="#cb51-928" aria-hidden="true" tabindex="-1"></a>  stats <span class="ot">&lt;-</span> <span class="fu">lapply</span>(inrange, tss) <span class="sc">%&gt;%</span> bind_rows <span class="sc">%&gt;%</span></span>
+<span id="cb51-929"><a href="#cb51-929" aria-hidden="true" tabindex="-1"></a>    <span class="fu">pivot_longer</span>(<span class="sc">!</span>threshold, <span class="at">names_to=</span><span class="st">"metric"</span>, <span class="at">values_to=</span><span class="st">"value"</span>)</span>
+<span id="cb51-930"><a href="#cb51-930" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(stats)</span>
+<span id="cb51-931"><a href="#cb51-931" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-932"><a href="#cb51-932" aria-hidden="true" tabindex="-1"></a>stats_0 <span class="ot">&lt;-</span> <span class="fu">range_f1</span>(mrg_blast)</span>
+<span id="cb51-933"><a href="#cb51-933" aria-hidden="true" tabindex="-1"></a>g_stats_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(stats_0, <span class="fu">aes</span>(<span class="at">x=</span>threshold, <span class="at">y=</span>value, <span class="at">color=</span>metric)) <span class="sc">+</span></span>
+<span id="cb51-934"><a href="#cb51-934" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">color =</span> <span class="st">"red"</span>, <span class="at">linetype =</span> <span class="st">"dashed"</span>) <span class="sc">+</span></span>
+<span id="cb51-935"><a href="#cb51-935" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
+<span id="cb51-936"><a href="#cb51-936" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"Value"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-937"><a href="#cb51-937" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Adjusted Score Threshold"</span>, <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-938"><a href="#cb51-938" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette=</span><span class="st">"Dark2"</span>) <span class="sc">+</span></span>
+<span id="cb51-939"><a href="#cb51-939" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb51-940"><a href="#cb51-940" aria-hidden="true" tabindex="-1"></a>g_stats_0</span>
+<span id="cb51-941"><a href="#cb51-941" aria-hidden="true" tabindex="-1"></a>stats_0 <span class="sc">%&gt;%</span> <span class="fu">filter</span>(threshold <span class="sc">==</span> <span class="dv">20</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-942"><a href="#cb51-942" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">Threshold=</span>threshold, <span class="at">Metric=</span>metric, <span class="at">Value=</span>value)</span>
+<span id="cb51-943"><a href="#cb51-943" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-944"><a href="#cb51-944" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-945"><a href="#cb51-945" aria-hidden="true" tabindex="-1"></a>Looking into the composition of different read groups, the notable observation for me is the high prevalence of Pigeon torque teno virus among high-scoring false positives, with 77 such read pairs. BLAST maps these not to viruses but to their most common hosts, i.e. assorted species of pigeon. That said, the number of false positive PTTV reads is substantially exceeded by the number of true-positive PTTV reads (1883), which do map to appropriate viruses according to BLAST, so the presence of a comparatively small number of false positives seems unlikely to cause too much distortion.</span>
+<span id="cb51-946"><a href="#cb51-946" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-949"><a href="#cb51-949" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-950"><a href="#cb51-950" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fp</span></span>
+<span id="cb51-951"><a href="#cb51-951" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 5</span></span>
+<span id="cb51-952"><a href="#cb51-952" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-953"><a href="#cb51-953" aria-hidden="true" tabindex="-1"></a>major_threshold <span class="ot">&lt;-</span> <span class="fl">0.04</span></span>
+<span id="cb51-954"><a href="#cb51-954" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-955"><a href="#cb51-955" aria-hidden="true" tabindex="-1"></a><span class="co"># Add missing viral taxa</span></span>
+<span id="cb51-956"><a href="#cb51-956" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">211787</span>] <span class="ot">&lt;-</span> <span class="st">"Human papillomavirus type 92"</span></span>
+<span id="cb51-957"><a href="#cb51-957" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">509154</span>] <span class="ot">&lt;-</span> <span class="st">"Porcine endogenous retrovirus C"</span></span>
+<span id="cb51-958"><a href="#cb51-958" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">493803</span>] <span class="ot">&lt;-</span> <span class="st">"Merkel cell polyomavirus"</span></span>
+<span id="cb51-959"><a href="#cb51-959" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">427343</span>] <span class="ot">&lt;-</span> <span class="st">"Human papillomavirus 107"</span></span>
+<span id="cb51-960"><a href="#cb51-960" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">194958</span>] <span class="ot">&lt;-</span> <span class="st">"Porcine endogenous retrovirus A"</span></span>
+<span id="cb51-961"><a href="#cb51-961" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">340907</span>] <span class="ot">&lt;-</span> <span class="st">"Papiine alphaherpesvirus 2"</span></span>
+<span id="cb51-962"><a href="#cb51-962" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">194959</span>] <span class="ot">&lt;-</span> <span class="st">"Porcine endogenous retrovirus B"</span></span>
+<span id="cb51-963"><a href="#cb51-963" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-964"><a href="#cb51-964" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-965"><a href="#cb51-965" aria-hidden="true" tabindex="-1"></a><span class="co"># Prepare data</span></span>
+<span id="cb51-966"><a href="#cb51-966" aria-hidden="true" tabindex="-1"></a>fp <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> </span>
+<span id="cb51-967"><a href="#cb51-967" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, highscore, taxid_best) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> </span>
+<span id="cb51-968"><a href="#cb51-968" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, highscore) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p=</span>n<span class="sc">/</span><span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-969"><a href="#cb51-969" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">taxid =</span> taxid_best) <span class="sc">%&gt;%</span></span>
+<span id="cb51-970"><a href="#cb51-970" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(viral_taxa, <span class="at">by=</span><span class="st">"taxid"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-971"><a href="#cb51-971" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p))</span>
+<span id="cb51-972"><a href="#cb51-972" aria-hidden="true" tabindex="-1"></a>fp_major_tab <span class="ot">&lt;-</span> fp <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p <span class="sc">&gt;</span> major_threshold) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(p))</span>
+<span id="cb51-973"><a href="#cb51-973" aria-hidden="true" tabindex="-1"></a>fp_major_list <span class="ot">&lt;-</span> fp_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name) <span class="sc">%&gt;%</span> sort <span class="sc">%&gt;%</span> unique <span class="sc">%&gt;%</span> <span class="fu">c</span>(., <span class="st">"Other"</span>)</span>
+<span id="cb51-974"><a href="#cb51-974" aria-hidden="true" tabindex="-1"></a>fp_major <span class="ot">&lt;-</span> fp <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">major =</span> p <span class="sc">&gt;</span> major_threshold) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-975"><a href="#cb51-975" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(major, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-976"><a href="#cb51-976" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, highscore, name_display) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-977"><a href="#cb51-977" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n=</span><span class="fu">sum</span>(n), <span class="at">p=</span><span class="fu">sum</span>(p), <span class="at">.groups =</span> <span class="st">"drop"</span>)  <span class="sc">%&gt;%</span></span>
+<span id="cb51-978"><a href="#cb51-978" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, <span class="at">levels =</span> fp_major_list),</span>
+<span id="cb51-979"><a href="#cb51-979" aria-hidden="true" tabindex="-1"></a>         <span class="at">score_display =</span> <span class="fu">ifelse</span>(highscore, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span>),</span>
+<span id="cb51-980"><a href="#cb51-980" aria-hidden="true" tabindex="-1"></a>         <span class="at">status_display =</span> <span class="fu">ifelse</span>(viral_status_out, <span class="st">"True positive"</span>, <span class="st">"False positive"</span>))</span>
+<span id="cb51-981"><a href="#cb51-981" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-982"><a href="#cb51-982" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-983"><a href="#cb51-983" aria-hidden="true" tabindex="-1"></a>g_fp <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(fp_major, <span class="fu">aes</span>(<span class="at">x=</span>score_display, <span class="at">y=</span>p, <span class="at">fill=</span>name_display)) <span class="sc">+</span></span>
+<span id="cb51-984"><a href="#cb51-984" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-985"><a href="#cb51-985" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_discrete</span>(<span class="at">name =</span> <span class="st">"True positive?"</span>) <span class="sc">+</span></span>
+<span id="cb51-986"><a href="#cb51-986" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"% reads"</span>, <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb51-987"><a href="#cb51-987" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-988"><a href="#cb51-988" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> palette_viral, <span class="at">name =</span> <span class="st">"Viral</span><span class="sc">\n</span><span class="st">taxon"</span>) <span class="sc">+</span></span>
+<span id="cb51-989"><a href="#cb51-989" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(.<span class="sc">~</span>status_display) <span class="sc">+</span></span>
+<span id="cb51-990"><a href="#cb51-990" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
+<span id="cb51-991"><a href="#cb51-991" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb51-992"><a href="#cb51-992" aria-hidden="true" tabindex="-1"></a>g_fp</span>
+<span id="cb51-993"><a href="#cb51-993" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-994"><a href="#cb51-994" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-995"><a href="#cb51-995" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-998"><a href="#cb51-998" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-999"><a href="#cb51-999" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: ptt-blast-hits</span></span>
+<span id="cb51-1000"><a href="#cb51-1000" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1001"><a href="#cb51-1001" aria-hidden="true" tabindex="-1"></a><span class="co"># Configure</span></span>
+<span id="cb51-1002"><a href="#cb51-1002" aria-hidden="true" tabindex="-1"></a>ref_taxid_ptt <span class="ot">&lt;-</span> <span class="dv">2233536</span></span>
+<span id="cb51-1003"><a href="#cb51-1003" aria-hidden="true" tabindex="-1"></a>p_threshold <span class="ot">&lt;-</span> <span class="fl">0.3</span></span>
+<span id="cb51-1004"><a href="#cb51-1004" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1005"><a href="#cb51-1005" aria-hidden="true" tabindex="-1"></a><span class="co"># Get taxon names</span></span>
+<span id="cb51-1006"><a href="#cb51-1006" aria-hidden="true" tabindex="-1"></a>tax_names_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir_base, <span class="st">"taxid-names.tsv.gz"</span>)</span>
+<span id="cb51-1007"><a href="#cb51-1007" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(tax_names_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb51-1008"><a href="#cb51-1008" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1009"><a href="#cb51-1009" aria-hidden="true" tabindex="-1"></a><span class="co"># Add missing names</span></span>
+<span id="cb51-1010"><a href="#cb51-1010" aria-hidden="true" tabindex="-1"></a>tax_names_new <span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>staxid, <span class="sc">~</span>name,</span>
+<span id="cb51-1011"><a href="#cb51-1011" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3050295</span>, <span class="st">"Cytomegalovirus humanbeta5"</span>,</span>
+<span id="cb51-1012"><a href="#cb51-1012" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">459231</span>, <span class="st">"FLAG-tagging vector pFLAG97-TSR"</span>,</span>
+<span id="cb51-1013"><a href="#cb51-1013" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3082113</span>, <span class="st">"Rangifer tarandus platyrhynchus"</span>,</span>
+<span id="cb51-1014"><a href="#cb51-1014" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3119969</span>, <span class="st">"Bubalus kerabau"</span>,</span>
+<span id="cb51-1015"><a href="#cb51-1015" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">177155</span>, <span class="st">"Streptopelia turtur"</span>,</span>
+<span id="cb51-1016"><a href="#cb51-1016" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">187126</span>, <span class="st">"Nesoenas mayeri"</span></span>
+<span id="cb51-1017"><a href="#cb51-1017" aria-hidden="true" tabindex="-1"></a>                         )</span>
+<span id="cb51-1018"><a href="#cb51-1018" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> tax_names_new <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="sc">!</span> staxid <span class="sc">%in%</span> tax_names<span class="sc">$</span>staxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1019"><a href="#cb51-1019" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bind_rows</span>(tax_names) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(staxid)</span>
+<span id="cb51-1020"><a href="#cb51-1020" aria-hidden="true" tabindex="-1"></a>ref_name_ptt <span class="ot">&lt;-</span> tax_names <span class="sc">%&gt;%</span> <span class="fu">filter</span>(staxid <span class="sc">==</span> ref_taxid_ptt) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb51-1021"><a href="#cb51-1021" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1022"><a href="#cb51-1022" aria-hidden="true" tabindex="-1"></a><span class="co"># Get major matches</span></span>
+<span id="cb51-1023"><a href="#cb51-1023" aria-hidden="true" tabindex="-1"></a>mrg_staxid <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid_best <span class="sc">==</span> ref_taxid_ptt) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1024"><a href="#cb51-1024" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>(highscore, viral_status_out) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">n_seq =</span> <span class="fu">n</span>())</span>
+<span id="cb51-1025"><a href="#cb51-1025" aria-hidden="true" tabindex="-1"></a>fp_staxid <span class="ot">&lt;-</span> mrg_staxid <span class="sc">%&gt;%</span></span>
+<span id="cb51-1026"><a href="#cb51-1026" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(blast_results_paired, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1027"><a href="#cb51-1027" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">staxid =</span> <span class="fu">as.integer</span>(staxid)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1028"><a href="#cb51-1028" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names, <span class="at">by=</span><span class="st">"staxid"</span>) <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">sname=</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1029"><a href="#cb51-1029" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">taxid_best=</span>staxid), <span class="at">by=</span><span class="st">"taxid_best"</span>)</span>
+<span id="cb51-1030"><a href="#cb51-1030" aria-hidden="true" tabindex="-1"></a>fp_staxid_count <span class="ot">&lt;-</span> fp_staxid <span class="sc">%&gt;%</span></span>
+<span id="cb51-1031"><a href="#cb51-1031" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, highscore, </span>
+<span id="cb51-1032"><a href="#cb51-1032" aria-hidden="true" tabindex="-1"></a>           taxid_best, name, staxid, sname, n_seq) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1033"><a href="#cb51-1033" aria-hidden="true" tabindex="-1"></a>  count <span class="sc">%&gt;%</span></span>
+<span id="cb51-1034"><a href="#cb51-1034" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, highscore, taxid_best, name) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1035"><a href="#cb51-1035" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p=</span>n<span class="sc">/</span>n_seq)</span>
+<span id="cb51-1036"><a href="#cb51-1036" aria-hidden="true" tabindex="-1"></a>fp_staxid_count_major <span class="ot">&lt;-</span> fp_staxid_count <span class="sc">%&gt;%</span></span>
+<span id="cb51-1037"><a href="#cb51-1037" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(n<span class="sc">&gt;</span><span class="dv">1</span>, p<span class="sc">&gt;</span>p_threshold, <span class="sc">!</span><span class="fu">is.na</span>(staxid)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1038"><a href="#cb51-1038" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">score_display =</span> <span class="fu">ifelse</span>(highscore, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span>),</span>
+<span id="cb51-1039"><a href="#cb51-1039" aria-hidden="true" tabindex="-1"></a>         <span class="at">status_display =</span> <span class="fu">ifelse</span>(viral_status_out, </span>
+<span id="cb51-1040"><a href="#cb51-1040" aria-hidden="true" tabindex="-1"></a>                                 <span class="st">"True positive"</span>, <span class="st">"False positive"</span>))</span>
+<span id="cb51-1041"><a href="#cb51-1041" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1042"><a href="#cb51-1042" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1043"><a href="#cb51-1043" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(fp_staxid_count_major, <span class="fu">aes</span>(<span class="at">x=</span>p, <span class="at">y=</span>sname)) <span class="sc">+</span> </span>
+<span id="cb51-1044"><a href="#cb51-1044" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>() <span class="sc">+</span> </span>
+<span id="cb51-1045"><a href="#cb51-1045" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(status_display<span class="sc">~</span>score_display, <span class="at">scales=</span><span class="st">"free"</span>,</span>
+<span id="cb51-1046"><a href="#cb51-1046" aria-hidden="true" tabindex="-1"></a>             <span class="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line =</span> <span class="cn">FALSE</span>)) <span class="sc">+</span></span>
+<span id="cb51-1047"><a href="#cb51-1047" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"% mapped reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-1048"><a href="#cb51-1048" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-1049"><a href="#cb51-1049" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="fu">paste0</span>(ref_name_ptt, <span class="st">" (taxid "</span>, ref_taxid_ptt, <span class="st">")"</span>)) <span class="sc">+</span></span>
+<span id="cb51-1050"><a href="#cb51-1050" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(</span>
+<span id="cb51-1051"><a href="#cb51-1051" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.title.y =</span> <span class="fu">element_blank</span>(),</span>
+<span id="cb51-1052"><a href="#cb51-1052" aria-hidden="true" tabindex="-1"></a>    <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="fl">1.4</span>), <span class="at">hjust=</span><span class="dv">0</span>, <span class="at">face=</span><span class="st">"plain"</span>))</span>
+<span id="cb51-1053"><a href="#cb51-1053" aria-hidden="true" tabindex="-1"></a>g</span>
+<span id="cb51-1054"><a href="#cb51-1054" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1055"><a href="#cb51-1055" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1056"><a href="#cb51-1056" aria-hidden="true" tabindex="-1"></a><span class="fu"># Human-infecting viruses: overall relative abundance</span></span>
+<span id="cb51-1057"><a href="#cb51-1057" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1060"><a href="#cb51-1060" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1061"><a href="#cb51-1061" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: count-hv-reads</span></span>
+<span id="cb51-1062"><a href="#cb51-1062" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1063"><a href="#cb51-1063" aria-hidden="true" tabindex="-1"></a><span class="co"># Get raw read counts</span></span>
+<span id="cb51-1064"><a href="#cb51-1064" aria-hidden="true" tabindex="-1"></a>read_counts_raw <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span></span>
+<span id="cb51-1065"><a href="#cb51-1065" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, date_alias, city, <span class="at">n_reads_raw =</span> n_read_pairs)</span>
+<span id="cb51-1066"><a href="#cb51-1066" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1067"><a href="#cb51-1067" aria-hidden="true" tabindex="-1"></a><span class="co"># Get HV read counts</span></span>
+<span id="cb51-1068"><a href="#cb51-1068" aria-hidden="true" tabindex="-1"></a>mrg_hv <span class="ot">&lt;-</span> mrg <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">hv_status =</span> assigned_hv <span class="sc">|</span> hit_hv <span class="sc">|</span> highscore) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1069"><a href="#cb51-1069" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">taxid_all =</span> taxid, <span class="at">taxid =</span> taxid_best)</span>
+<span id="cb51-1070"><a href="#cb51-1070" aria-hidden="true" tabindex="-1"></a>read_counts_hv <span class="ot">&lt;-</span> mrg_hv <span class="sc">%&gt;%</span> <span class="fu">filter</span>(hv_status) <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1071"><a href="#cb51-1071" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name=</span><span class="st">"n_reads_hv"</span>)</span>
+<span id="cb51-1072"><a href="#cb51-1072" aria-hidden="true" tabindex="-1"></a>read_counts <span class="ot">&lt;-</span> read_counts_raw <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(read_counts_hv, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1073"><a href="#cb51-1073" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_reads_hv =</span> <span class="fu">replace_na</span>(n_reads_hv, <span class="dv">0</span>))</span>
+<span id="cb51-1074"><a href="#cb51-1074" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1075"><a href="#cb51-1075" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate</span></span>
+<span id="cb51-1076"><a href="#cb51-1076" aria-hidden="true" tabindex="-1"></a>read_counts_city <span class="ot">&lt;-</span> read_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1077"><a href="#cb51-1077" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_raw =</span> <span class="fu">sum</span>(n_reads_raw),</span>
+<span id="cb51-1078"><a href="#cb51-1078" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1079"><a href="#cb51-1079" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample=</span> <span class="st">"All samples"</span>, <span class="at">date_alias =</span> <span class="st">"All dates"</span>)</span>
+<span id="cb51-1080"><a href="#cb51-1080" aria-hidden="true" tabindex="-1"></a>read_counts_total <span class="ot">&lt;-</span> read_counts_city <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample, date_alias) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1081"><a href="#cb51-1081" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_raw =</span> <span class="fu">sum</span>(n_reads_raw),</span>
+<span id="cb51-1082"><a href="#cb51-1082" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1083"><a href="#cb51-1083" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">city =</span> <span class="st">"All cities"</span>)</span>
+<span id="cb51-1084"><a href="#cb51-1084" aria-hidden="true" tabindex="-1"></a>read_counts_agg <span class="ot">&lt;-</span> read_counts_city <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1085"><a href="#cb51-1085" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bind_rows</span>(read_counts_total) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1086"><a href="#cb51-1086" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span>n_reads_raw,</span>
+<span id="cb51-1087"><a href="#cb51-1087" aria-hidden="true" tabindex="-1"></a>         <span class="at">city =</span> <span class="fu">fct_inorder</span>(city))</span>
+<span id="cb51-1088"><a href="#cb51-1088" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1089"><a href="#cb51-1089" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1090"><a href="#cb51-1090" aria-hidden="true" tabindex="-1"></a>Applying a disjunctive cutoff at S=20 identifies 23,191 read pairs as human-viral. This gives an overall relative HV abundance of $1.73 \times 10^{-5}$.</span>
+<span id="cb51-1091"><a href="#cb51-1091" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1094"><a href="#cb51-1094" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1095"><a href="#cb51-1095" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-hv-ra</span></span>
+<span id="cb51-1096"><a href="#cb51-1096" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb51-1097"><a href="#cb51-1097" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize</span></span>
+<span id="cb51-1098"><a href="#cb51-1098" aria-hidden="true" tabindex="-1"></a>g_phv_agg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(read_counts_agg, <span class="fu">aes</span>(<span class="at">x=</span>city, <span class="at">color=</span>city)) <span class="sc">+</span></span>
+<span id="cb51-1099"><a href="#cb51-1099" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">y=</span>p_reads_hv)) <span class="sc">+</span></span>
+<span id="cb51-1100"><a href="#cb51-1100" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_log10</span>(<span class="st">"Relative abundance of human virus reads"</span>) <span class="sc">+</span></span>
+<span id="cb51-1101"><a href="#cb51-1101" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_discrete</span>(<span class="at">name=</span><span class="st">"Collection Date"</span>) <span class="sc">+</span></span>
+<span id="cb51-1102"><a href="#cb51-1102" aria-hidden="true" tabindex="-1"></a>  <span class="co">#facet_grid(.~sample_type, scales = "free", space = "free_x") +</span></span>
+<span id="cb51-1103"><a href="#cb51-1103" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_city</span>() <span class="sc">+</span> theme_rotate</span>
+<span id="cb51-1104"><a href="#cb51-1104" aria-hidden="true" tabindex="-1"></a>g_phv_agg</span>
+<span id="cb51-1105"><a href="#cb51-1105" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1106"><a href="#cb51-1106" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1107"><a href="#cb51-1107" aria-hidden="true" tabindex="-1"></a>This is lower than for DNA reads from other air-sampling datasets I've analyzed, but not drastically so:</span>
+<span id="cb51-1108"><a href="#cb51-1108" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1111"><a href="#cb51-1111" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1112"><a href="#cb51-1112" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: ra-hv-past</span></span>
+<span id="cb51-1113"><a href="#cb51-1113" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1114"><a href="#cb51-1114" aria-hidden="true" tabindex="-1"></a><span class="co"># Collate past RA values</span></span>
+<span id="cb51-1115"><a href="#cb51-1115" aria-hidden="true" tabindex="-1"></a>ra_past <span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>dataset, <span class="sc">~</span>ra, <span class="sc">~</span>na_type, <span class="sc">~</span>panel_enriched,</span>
+<span id="cb51-1116"><a href="#cb51-1116" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Brumfield"</span>, <span class="fl">5e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb51-1117"><a href="#cb51-1117" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Brumfield"</span>, <span class="fl">3.66e-7</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb51-1118"><a href="#cb51-1118" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Spurbeck"</span>, <span class="fl">5.44e-6</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb51-1119"><a href="#cb51-1119" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Yang"</span>, <span class="fl">3.62e-4</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb51-1120"><a href="#cb51-1120" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Rothman (unenriched)"</span>, <span class="fl">1.87e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb51-1121"><a href="#cb51-1121" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Rothman (panel-enriched)"</span>, <span class="fl">3.3e-5</span>, <span class="st">"RNA"</span>, <span class="cn">TRUE</span>,</span>
+<span id="cb51-1122"><a href="#cb51-1122" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Crits-Christoph (unenriched)"</span>, <span class="fl">1.37e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb51-1123"><a href="#cb51-1123" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Crits-Christoph (panel-enriched)"</span>, <span class="fl">1.26e-2</span>, <span class="st">"RNA"</span>, <span class="cn">TRUE</span>,</span>
+<span id="cb51-1124"><a href="#cb51-1124" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Prussin (non-control)"</span>, <span class="fl">1.63e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb51-1125"><a href="#cb51-1125" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Prussin (non-control)"</span>, <span class="fl">4.16e-5</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb51-1126"><a href="#cb51-1126" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Rosario (non-control)"</span>, <span class="fl">1.21e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb51-1127"><a href="#cb51-1127" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Rosario (non-control)"</span>, <span class="fl">1.50e-4</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span></span>
+<span id="cb51-1128"><a href="#cb51-1128" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb51-1129"><a href="#cb51-1129" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1130"><a href="#cb51-1130" aria-hidden="true" tabindex="-1"></a><span class="co"># Collate new RA values</span></span>
+<span id="cb51-1131"><a href="#cb51-1131" aria-hidden="true" tabindex="-1"></a>ra_new <span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>dataset, <span class="sc">~</span>ra, <span class="sc">~</span>na_type, <span class="sc">~</span>panel_enriched,</span>
+<span id="cb51-1132"><a href="#cb51-1132" aria-hidden="true" tabindex="-1"></a>                  <span class="st">"Leung"</span>, <span class="fl">1.73e-5</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span>)</span>
+<span id="cb51-1133"><a href="#cb51-1133" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1134"><a href="#cb51-1134" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1135"><a href="#cb51-1135" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1136"><a href="#cb51-1136" aria-hidden="true" tabindex="-1"></a>scale_color_na <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_color_brewer, <span class="at">palette=</span><span class="st">"Set1"</span>,</span>
+<span id="cb51-1137"><a href="#cb51-1137" aria-hidden="true" tabindex="-1"></a>                                 <span class="at">name=</span><span class="st">"Nucleic acid type"</span>)</span>
+<span id="cb51-1138"><a href="#cb51-1138" aria-hidden="true" tabindex="-1"></a>ra_comp <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(ra_past, ra_new) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">dataset =</span> <span class="fu">fct_inorder</span>(dataset))</span>
+<span id="cb51-1139"><a href="#cb51-1139" aria-hidden="true" tabindex="-1"></a>g_ra_comp <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(ra_comp, <span class="fu">aes</span>(<span class="at">y=</span>dataset, <span class="at">x=</span>ra, <span class="at">color=</span>na_type)) <span class="sc">+</span></span>
+<span id="cb51-1140"><a href="#cb51-1140" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>() <span class="sc">+</span></span>
+<span id="cb51-1141"><a href="#cb51-1141" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span></span>
+<span id="cb51-1142"><a href="#cb51-1142" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_log10</span>(<span class="at">name=</span><span class="st">"Relative abundance of human virus reads"</span>) <span class="sc">+</span></span>
+<span id="cb51-1143"><a href="#cb51-1143" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.title.y =</span> <span class="fu">element_blank</span>())</span>
+<span id="cb51-1144"><a href="#cb51-1144" aria-hidden="true" tabindex="-1"></a>g_ra_comp</span>
+<span id="cb51-1145"><a href="#cb51-1145" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1146"><a href="#cb51-1146" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1147"><a href="#cb51-1147" aria-hidden="true" tabindex="-1"></a><span class="fu"># Human-infecting viruses: taxonomy and composition</span></span>
+<span id="cb51-1148"><a href="#cb51-1148" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1149"><a href="#cb51-1149" aria-hidden="true" tabindex="-1"></a>At the family level, most samples across all cities are dominated by *Papillomaviridae*, *Herpesviridae*, *Anelloviridae*, *Polyomaviridae, and to a lesser extent* *Poxviridae*:</span>
+<span id="cb51-1150"><a href="#cb51-1150" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1153"><a href="#cb51-1153" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1154"><a href="#cb51-1154" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: raise-hv-taxa</span></span>
+<span id="cb51-1155"><a href="#cb51-1155" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1156"><a href="#cb51-1156" aria-hidden="true" tabindex="-1"></a><span class="co"># Get viral taxon names for putative HV reads</span></span>
+<span id="cb51-1157"><a href="#cb51-1157" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">249588</span>] <span class="ot">&lt;-</span> <span class="st">"Mamastrovirus"</span></span>
+<span id="cb51-1158"><a href="#cb51-1158" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">194960</span>] <span class="ot">&lt;-</span> <span class="st">"Kobuvirus"</span></span>
+<span id="cb51-1159"><a href="#cb51-1159" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">688449</span>] <span class="ot">&lt;-</span> <span class="st">"Salivirus"</span></span>
+<span id="cb51-1160"><a href="#cb51-1160" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">585893</span>] <span class="ot">&lt;-</span> <span class="st">"Picobirnaviridae"</span></span>
+<span id="cb51-1161"><a href="#cb51-1161" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333922</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus"</span></span>
+<span id="cb51-1162"><a href="#cb51-1162" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334207</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 3"</span></span>
+<span id="cb51-1163"><a href="#cb51-1163" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">369960</span>] <span class="ot">&lt;-</span> <span class="st">"Porcine type-C oncovirus"</span></span>
+<span id="cb51-1164"><a href="#cb51-1164" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333924</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 2"</span></span>
+<span id="cb51-1165"><a href="#cb51-1165" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">687329</span>] <span class="ot">&lt;-</span> <span class="st">"Anelloviridae"</span></span>
+<span id="cb51-1166"><a href="#cb51-1166" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">325455</span>] <span class="ot">&lt;-</span> <span class="st">"Gammapapillomavirus"</span></span>
+<span id="cb51-1167"><a href="#cb51-1167" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333750</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus"</span></span>
+<span id="cb51-1168"><a href="#cb51-1168" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">694002</span>] <span class="ot">&lt;-</span> <span class="st">"Betacoronavirus"</span></span>
+<span id="cb51-1169"><a href="#cb51-1169" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334202</span>] <span class="ot">&lt;-</span> <span class="st">"Mupapillomavirus"</span></span>
+<span id="cb51-1170"><a href="#cb51-1170" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">197911</span>] <span class="ot">&lt;-</span> <span class="st">"Alphainfluenzavirus"</span></span>
+<span id="cb51-1171"><a href="#cb51-1171" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">186938</span>] <span class="ot">&lt;-</span> <span class="st">"Respirovirus"</span></span>
+<span id="cb51-1172"><a href="#cb51-1172" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333926</span>] <span class="ot">&lt;-</span> <span class="st">"Gammapapillomavirus 1"</span></span>
+<span id="cb51-1173"><a href="#cb51-1173" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">337051</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 1"</span></span>
+<span id="cb51-1174"><a href="#cb51-1174" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">337043</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus 4"</span></span>
+<span id="cb51-1175"><a href="#cb51-1175" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">694003</span>] <span class="ot">&lt;-</span> <span class="st">"Betacoronavirus 1"</span></span>
+<span id="cb51-1176"><a href="#cb51-1176" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334204</span>] <span class="ot">&lt;-</span> <span class="st">"Mupapillomavirus 2"</span></span>
+<span id="cb51-1177"><a href="#cb51-1177" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334208</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span id="cb51-1178"><a href="#cb51-1178" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333928</span>] <span class="ot">&lt;-</span> <span class="st">"Gammapapillomavirus 2"</span></span>
+<span id="cb51-1179"><a href="#cb51-1179" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">337039</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus 2"</span></span>
+<span id="cb51-1180"><a href="#cb51-1180" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333929</span>] <span class="ot">&lt;-</span> <span class="st">"Gammapapillomavirus 3"</span></span>
+<span id="cb51-1181"><a href="#cb51-1181" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">337042</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus 7"</span></span>
+<span id="cb51-1182"><a href="#cb51-1182" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334203</span>] <span class="ot">&lt;-</span> <span class="st">"Mupapillomavirus 1"</span></span>
+<span id="cb51-1183"><a href="#cb51-1183" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333757</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus 8"</span></span>
+<span id="cb51-1184"><a href="#cb51-1184" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">337050</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus 6"</span></span>
+<span id="cb51-1185"><a href="#cb51-1185" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333767</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus 3"</span></span>
+<span id="cb51-1186"><a href="#cb51-1186" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333754</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus 10"</span></span>
+<span id="cb51-1187"><a href="#cb51-1187" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">687363</span>] <span class="ot">&lt;-</span> <span class="st">"Torque teno virus 24"</span></span>
+<span id="cb51-1188"><a href="#cb51-1188" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">687342</span>] <span class="ot">&lt;-</span> <span class="st">"Torque teno virus 3"</span></span>
+<span id="cb51-1189"><a href="#cb51-1189" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">687359</span>] <span class="ot">&lt;-</span> <span class="st">"Torque teno virus 20"</span></span>
+<span id="cb51-1190"><a href="#cb51-1190" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">194441</span>] <span class="ot">&lt;-</span> <span class="st">"Primate T-lymphotropic virus 2"</span></span>
+<span id="cb51-1191"><a href="#cb51-1191" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334209</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 5"</span></span>
+<span id="cb51-1192"><a href="#cb51-1192" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1193"><a href="#cb51-1193" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1194"><a href="#cb51-1194" aria-hidden="true" tabindex="-1"></a>mrg_hv_named <span class="ot">&lt;-</span> mrg_hv <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(viral_taxa, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb51-1195"><a href="#cb51-1195" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1196"><a href="#cb51-1196" aria-hidden="true" tabindex="-1"></a><span class="co"># Discover viral species &amp; genera for HV reads</span></span>
+<span id="cb51-1197"><a href="#cb51-1197" aria-hidden="true" tabindex="-1"></a>raise_rank <span class="ot">&lt;-</span> <span class="cf">function</span>(read_db, taxid_db, <span class="at">out_rank =</span> <span class="st">"species"</span>, <span class="at">verbose =</span> <span class="cn">FALSE</span>){</span>
+<span id="cb51-1198"><a href="#cb51-1198" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Get higher ranks than search rank</span></span>
+<span id="cb51-1199"><a href="#cb51-1199" aria-hidden="true" tabindex="-1"></a>  ranks <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"subspecies"</span>, <span class="st">"species"</span>, <span class="st">"subgenus"</span>, <span class="st">"genus"</span>, <span class="st">"subfamily"</span>, <span class="st">"family"</span>, <span class="st">"suborder"</span>, <span class="st">"order"</span>, <span class="st">"class"</span>, <span class="st">"subphylum"</span>, <span class="st">"phylum"</span>, <span class="st">"kingdom"</span>, <span class="st">"superkingdom"</span>)</span>
+<span id="cb51-1200"><a href="#cb51-1200" aria-hidden="true" tabindex="-1"></a>  rank_match <span class="ot">&lt;-</span> <span class="fu">which.max</span>(ranks <span class="sc">==</span> out_rank)</span>
+<span id="cb51-1201"><a href="#cb51-1201" aria-hidden="true" tabindex="-1"></a>  high_ranks <span class="ot">&lt;-</span> ranks[rank_match<span class="sc">:</span><span class="fu">length</span>(ranks)]</span>
+<span id="cb51-1202"><a href="#cb51-1202" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Merge read DB and taxid DB</span></span>
+<span id="cb51-1203"><a href="#cb51-1203" aria-hidden="true" tabindex="-1"></a>  reads <span class="ot">&lt;-</span> read_db <span class="sc">%&gt;%</span> <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1204"><a href="#cb51-1204" aria-hidden="true" tabindex="-1"></a>    <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb51-1205"><a href="#cb51-1205" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Extract sequences that are already at appropriate rank</span></span>
+<span id="cb51-1206"><a href="#cb51-1206" aria-hidden="true" tabindex="-1"></a>  reads_rank <span class="ot">&lt;-</span> <span class="fu">filter</span>(reads, rank <span class="sc">==</span> out_rank)</span>
+<span id="cb51-1207"><a href="#cb51-1207" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Drop sequences at a higher rank and return unclassified sequences</span></span>
+<span id="cb51-1208"><a href="#cb51-1208" aria-hidden="true" tabindex="-1"></a>  reads_norank <span class="ot">&lt;-</span> reads <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">!=</span> out_rank, <span class="sc">!</span>rank <span class="sc">%in%</span> high_ranks, <span class="sc">!</span><span class="fu">is.na</span>(taxid))</span>
+<span id="cb51-1209"><a href="#cb51-1209" aria-hidden="true" tabindex="-1"></a>  <span class="cf">while</span>(<span class="fu">nrow</span>(reads_norank) <span class="sc">&gt;</span> <span class="dv">0</span>){ <span class="co"># As long as there are unclassified sequences...</span></span>
+<span id="cb51-1210"><a href="#cb51-1210" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Promote read taxids and re-merge with taxid DB, then re-classify and filter</span></span>
+<span id="cb51-1211"><a href="#cb51-1211" aria-hidden="true" tabindex="-1"></a>    reads_remaining <span class="ot">&lt;-</span> reads_norank <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">taxid =</span> parent_taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1212"><a href="#cb51-1212" aria-hidden="true" tabindex="-1"></a>      <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1213"><a href="#cb51-1213" aria-hidden="true" tabindex="-1"></a>      <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb51-1214"><a href="#cb51-1214" aria-hidden="true" tabindex="-1"></a>    reads_rank <span class="ot">&lt;-</span> reads_remaining <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> out_rank) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1215"><a href="#cb51-1215" aria-hidden="true" tabindex="-1"></a>      <span class="fu">bind_rows</span>(reads_rank)</span>
+<span id="cb51-1216"><a href="#cb51-1216" aria-hidden="true" tabindex="-1"></a>    reads_norank <span class="ot">&lt;-</span> reads_remaining <span class="sc">%&gt;%</span></span>
+<span id="cb51-1217"><a href="#cb51-1217" aria-hidden="true" tabindex="-1"></a>      <span class="fu">filter</span>(rank <span class="sc">!=</span> out_rank, <span class="sc">!</span>rank <span class="sc">%in%</span> high_ranks, <span class="sc">!</span><span class="fu">is.na</span>(taxid))</span>
+<span id="cb51-1218"><a href="#cb51-1218" aria-hidden="true" tabindex="-1"></a>  }</span>
+<span id="cb51-1219"><a href="#cb51-1219" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Finally, extract and append reads that were excluded during the process</span></span>
+<span id="cb51-1220"><a href="#cb51-1220" aria-hidden="true" tabindex="-1"></a>  reads_dropped <span class="ot">&lt;-</span> reads <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="sc">!</span>seq_id <span class="sc">%in%</span> reads_rank<span class="sc">$</span>seq_id)</span>
+<span id="cb51-1221"><a href="#cb51-1221" aria-hidden="true" tabindex="-1"></a>  reads_out <span class="ot">&lt;-</span> reads_rank <span class="sc">%&gt;%</span> <span class="fu">bind_rows</span>(reads_dropped) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1222"><a href="#cb51-1222" aria-hidden="true" tabindex="-1"></a>    <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1223"><a href="#cb51-1223" aria-hidden="true" tabindex="-1"></a>    <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb51-1224"><a href="#cb51-1224" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(reads_out)</span>
+<span id="cb51-1225"><a href="#cb51-1225" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb51-1226"><a href="#cb51-1226" aria-hidden="true" tabindex="-1"></a>hv_reads_species <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"species"</span>)</span>
+<span id="cb51-1227"><a href="#cb51-1227" aria-hidden="true" tabindex="-1"></a>hv_reads_genus <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"genus"</span>)</span>
+<span id="cb51-1228"><a href="#cb51-1228" aria-hidden="true" tabindex="-1"></a>hv_reads_family <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"family"</span>)</span>
+<span id="cb51-1229"><a href="#cb51-1229" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1230"><a href="#cb51-1230" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1233"><a href="#cb51-1233" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1234"><a href="#cb51-1234" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-family</span></span>
+<span id="cb51-1235"><a href="#cb51-1235" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-1236"><a href="#cb51-1236" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 9</span></span>
+<span id="cb51-1237"><a href="#cb51-1237" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1238"><a href="#cb51-1238" aria-hidden="true" tabindex="-1"></a>threshold_major_family <span class="ot">&lt;-</span> <span class="fl">0.05</span></span>
+<span id="cb51-1239"><a href="#cb51-1239" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1240"><a href="#cb51-1240" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral family</span></span>
+<span id="cb51-1241"><a href="#cb51-1241" aria-hidden="true" tabindex="-1"></a>hv_family_counts <span class="ot">&lt;-</span> hv_reads_family <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1242"><a href="#cb51-1242" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1243"><a href="#cb51-1243" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1244"><a href="#cb51-1244" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1245"><a href="#cb51-1245" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb51-1246"><a href="#cb51-1246" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1247"><a href="#cb51-1247" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb51-1248"><a href="#cb51-1248" aria-hidden="true" tabindex="-1"></a>hv_family_major_tab <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1249"><a href="#cb51-1249" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1250"><a href="#cb51-1250" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> threshold_major_family)</span>
+<span id="cb51-1251"><a href="#cb51-1251" aria-hidden="true" tabindex="-1"></a>hv_family_counts_major <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span></span>
+<span id="cb51-1252"><a href="#cb51-1252" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_family_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1253"><a href="#cb51-1253" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1254"><a href="#cb51-1254" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
+<span id="cb51-1255"><a href="#cb51-1255" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1256"><a href="#cb51-1256" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb51-1257"><a href="#cb51-1257" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_family_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb51-1258"><a href="#cb51-1258" aria-hidden="true" tabindex="-1"></a>hv_family_counts_display <span class="ot">&lt;-</span> hv_family_counts_major <span class="sc">%&gt;%</span></span>
+<span id="cb51-1259"><a href="#cb51-1259" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_hv, <span class="at">classification =</span> name_display)</span>
+<span id="cb51-1260"><a href="#cb51-1260" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1261"><a href="#cb51-1261" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1262"><a href="#cb51-1262" aria-hidden="true" tabindex="-1"></a>g_hv_family <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-1263"><a href="#cb51-1263" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>hv_family_counts_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-1264"><a href="#cb51-1264" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb51-1265"><a href="#cb51-1265" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-1266"><a href="#cb51-1266" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-1267"><a href="#cb51-1267" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral family"</span>) <span class="sc">+</span></span>
+<span id="cb51-1268"><a href="#cb51-1268" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"Family composition of human-viral reads"</span>) <span class="sc">+</span></span>
+<span id="cb51-1269"><a href="#cb51-1269" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">4</span>)) <span class="sc">+</span></span>
+<span id="cb51-1270"><a href="#cb51-1270" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="fl">1.4</span>), <span class="at">hjust=</span><span class="dv">0</span>, <span class="at">face=</span><span class="st">"plain"</span>))</span>
+<span id="cb51-1271"><a href="#cb51-1271" aria-hidden="true" tabindex="-1"></a>g_hv_family</span>
+<span id="cb51-1272"><a href="#cb51-1272" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1273"><a href="#cb51-1273" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1274"><a href="#cb51-1274" aria-hidden="true" tabindex="-1"></a>In investigating individual viral families, to avoid distortions from a few rare reads, I restricted myself to samples where that family made up at least 10% of human-viral reads.</span>
+<span id="cb51-1275"><a href="#cb51-1275" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1276"><a href="#cb51-1276" aria-hidden="true" tabindex="-1"></a>As usual, *Papillomaviridae* reads are divided among many different viral species. In this case, Betapapillomavirus 1 and 2 are the most prevalent across samples, but many other alpha-, beta-, gamma-, and mupapillomaviruses are highly prevalent in at least some samples.</span>
+<span id="cb51-1277"><a href="#cb51-1277" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1280"><a href="#cb51-1280" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1281"><a href="#cb51-1281" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-species-papilloma</span></span>
+<span id="cb51-1282"><a href="#cb51-1282" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-1283"><a href="#cb51-1283" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 9</span></span>
+<span id="cb51-1284"><a href="#cb51-1284" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1285"><a href="#cb51-1285" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1286"><a href="#cb51-1286" aria-hidden="true" tabindex="-1"></a>threshold_major_species <span class="ot">&lt;-</span> <span class="fl">0.4</span></span>
+<span id="cb51-1287"><a href="#cb51-1287" aria-hidden="true" tabindex="-1"></a>taxid_papilloma <span class="ot">&lt;-</span> <span class="dv">151340</span></span>
+<span id="cb51-1288"><a href="#cb51-1288" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1289"><a href="#cb51-1289" aria-hidden="true" tabindex="-1"></a><span class="co"># Get set of Papillomaviridae reads</span></span>
+<span id="cb51-1290"><a href="#cb51-1290" aria-hidden="true" tabindex="-1"></a>papilloma_samples <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_papilloma) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1291"><a href="#cb51-1291" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;=</span> <span class="fl">0.1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1292"><a href="#cb51-1292" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(sample)</span>
+<span id="cb51-1293"><a href="#cb51-1293" aria-hidden="true" tabindex="-1"></a>papilloma_ids <span class="ot">&lt;-</span> hv_reads_family <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1294"><a href="#cb51-1294" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_papilloma, sample <span class="sc">%in%</span> papilloma_samples) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1295"><a href="#cb51-1295" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(seq_id)</span>
+<span id="cb51-1296"><a href="#cb51-1296" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1297"><a href="#cb51-1297" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each Papillomaviridae species</span></span>
+<span id="cb51-1298"><a href="#cb51-1298" aria-hidden="true" tabindex="-1"></a>papilloma_species_counts <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span></span>
+<span id="cb51-1299"><a href="#cb51-1299" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(seq_id <span class="sc">%in%</span> papilloma_ids) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1300"><a href="#cb51-1300" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1301"><a href="#cb51-1301" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1302"><a href="#cb51-1302" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1303"><a href="#cb51-1303" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_papilloma =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb51-1304"><a href="#cb51-1304" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1305"><a href="#cb51-1305" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb51-1306"><a href="#cb51-1306" aria-hidden="true" tabindex="-1"></a>papilloma_species_major_tab <span class="ot">&lt;-</span> papilloma_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1307"><a href="#cb51-1307" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_papilloma <span class="sc">==</span> <span class="fu">max</span>(p_reads_papilloma)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1308"><a href="#cb51-1308" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1309"><a href="#cb51-1309" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_papilloma)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1310"><a href="#cb51-1310" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_papilloma <span class="sc">&gt;</span> threshold_major_species)</span>
+<span id="cb51-1311"><a href="#cb51-1311" aria-hidden="true" tabindex="-1"></a>papilloma_species_counts_major <span class="ot">&lt;-</span> papilloma_species_counts <span class="sc">%&gt;%</span></span>
+<span id="cb51-1312"><a href="#cb51-1312" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> papilloma_species_major_tab<span class="sc">$</span>name, </span>
+<span id="cb51-1313"><a href="#cb51-1313" aria-hidden="true" tabindex="-1"></a>                               name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1314"><a href="#cb51-1314" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1315"><a href="#cb51-1315" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_papilloma =</span> <span class="fu">sum</span>(n_reads_hv),</span>
+<span id="cb51-1316"><a href="#cb51-1316" aria-hidden="true" tabindex="-1"></a>            <span class="at">p_reads_papilloma =</span> <span class="fu">sum</span>(p_reads_papilloma), </span>
+<span id="cb51-1317"><a href="#cb51-1317" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1318"><a href="#cb51-1318" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb51-1319"><a href="#cb51-1319" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(papilloma_species_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb51-1320"><a href="#cb51-1320" aria-hidden="true" tabindex="-1"></a>papilloma_species_counts_display <span class="ot">&lt;-</span> papilloma_species_counts_major <span class="sc">%&gt;%</span></span>
+<span id="cb51-1321"><a href="#cb51-1321" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_papilloma, <span class="at">classification =</span> name_display)</span>
+<span id="cb51-1322"><a href="#cb51-1322" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1323"><a href="#cb51-1323" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1324"><a href="#cb51-1324" aria-hidden="true" tabindex="-1"></a>g_papilloma_species <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-1325"><a href="#cb51-1325" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>papilloma_species_counts_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-1326"><a href="#cb51-1326" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Papillomaviridae Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb51-1327"><a href="#cb51-1327" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-1328"><a href="#cb51-1328" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-1329"><a href="#cb51-1329" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral species"</span>) <span class="sc">+</span></span>
+<span id="cb51-1330"><a href="#cb51-1330" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"Species composition of Papillomaviridae reads"</span>) <span class="sc">+</span></span>
+<span id="cb51-1331"><a href="#cb51-1331" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
+<span id="cb51-1332"><a href="#cb51-1332" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="fl">1.4</span>), <span class="at">hjust=</span><span class="dv">0</span>, <span class="at">face=</span><span class="st">"plain"</span>))</span>
+<span id="cb51-1333"><a href="#cb51-1333" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1334"><a href="#cb51-1334" aria-hidden="true" tabindex="-1"></a>g_papilloma_species</span>
+<span id="cb51-1335"><a href="#cb51-1335" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1336"><a href="#cb51-1336" aria-hidden="true" tabindex="-1"></a><span class="co"># Get most prominent species for text</span></span>
+<span id="cb51-1337"><a href="#cb51-1337" aria-hidden="true" tabindex="-1"></a>papilloma_species_collate <span class="ot">&lt;-</span> papilloma_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1338"><a href="#cb51-1338" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_tot =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_mean =</span> <span class="fu">mean</span>(p_reads_papilloma), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1339"><a href="#cb51-1339" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(n_reads_tot))</span>
+<span id="cb51-1340"><a href="#cb51-1340" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1341"><a href="#cb51-1341" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1342"><a href="#cb51-1342" aria-hidden="true" tabindex="-1"></a>In terms of total reads across samples, herpesviruses are dominated by Epstein-Barr virus (Human gammaherpesvirus 4), HSV-1 (Human alphaherpesvirus 1), and human cytomegalovirus (Human betaherpesvirus 5). However, numerous other herpesviruses are also present.</span>
+<span id="cb51-1343"><a href="#cb51-1343" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1346"><a href="#cb51-1346" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1347"><a href="#cb51-1347" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-species-herpes</span></span>
+<span id="cb51-1348"><a href="#cb51-1348" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-1349"><a href="#cb51-1349" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 9</span></span>
+<span id="cb51-1350"><a href="#cb51-1350" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1351"><a href="#cb51-1351" aria-hidden="true" tabindex="-1"></a>threshold_major_species <span class="ot">&lt;-</span> <span class="fl">0.4</span></span>
+<span id="cb51-1352"><a href="#cb51-1352" aria-hidden="true" tabindex="-1"></a>taxid_herpes <span class="ot">&lt;-</span> viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">"Herpesviridae"</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)</span>
+<span id="cb51-1353"><a href="#cb51-1353" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1354"><a href="#cb51-1354" aria-hidden="true" tabindex="-1"></a><span class="co"># Get set of herpesviridae reads</span></span>
+<span id="cb51-1355"><a href="#cb51-1355" aria-hidden="true" tabindex="-1"></a>herpes_samples <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_herpes) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1356"><a href="#cb51-1356" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;=</span> <span class="fl">0.1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1357"><a href="#cb51-1357" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(sample)</span>
+<span id="cb51-1358"><a href="#cb51-1358" aria-hidden="true" tabindex="-1"></a>herpes_ids <span class="ot">&lt;-</span> hv_reads_family <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1359"><a href="#cb51-1359" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_herpes, sample <span class="sc">%in%</span> herpes_samples) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1360"><a href="#cb51-1360" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(seq_id)</span>
+<span id="cb51-1361"><a href="#cb51-1361" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1362"><a href="#cb51-1362" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each herpesviridae species</span></span>
+<span id="cb51-1363"><a href="#cb51-1363" aria-hidden="true" tabindex="-1"></a>herpes_species_counts <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span></span>
+<span id="cb51-1364"><a href="#cb51-1364" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(seq_id <span class="sc">%in%</span> herpes_ids) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1365"><a href="#cb51-1365" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1366"><a href="#cb51-1366" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1367"><a href="#cb51-1367" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1368"><a href="#cb51-1368" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_herpes =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb51-1369"><a href="#cb51-1369" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1370"><a href="#cb51-1370" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb51-1371"><a href="#cb51-1371" aria-hidden="true" tabindex="-1"></a>herpes_species_major_tab <span class="ot">&lt;-</span> herpes_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1372"><a href="#cb51-1372" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_herpes <span class="sc">==</span> <span class="fu">max</span>(p_reads_herpes)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1373"><a href="#cb51-1373" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1374"><a href="#cb51-1374" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_herpes)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1375"><a href="#cb51-1375" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_herpes <span class="sc">&gt;</span> threshold_major_species)</span>
+<span id="cb51-1376"><a href="#cb51-1376" aria-hidden="true" tabindex="-1"></a>herpes_species_counts_major <span class="ot">&lt;-</span> herpes_species_counts <span class="sc">%&gt;%</span></span>
+<span id="cb51-1377"><a href="#cb51-1377" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> herpes_species_major_tab<span class="sc">$</span>name, </span>
+<span id="cb51-1378"><a href="#cb51-1378" aria-hidden="true" tabindex="-1"></a>                               name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1379"><a href="#cb51-1379" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1380"><a href="#cb51-1380" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_herpes =</span> <span class="fu">sum</span>(n_reads_hv),</span>
+<span id="cb51-1381"><a href="#cb51-1381" aria-hidden="true" tabindex="-1"></a>            <span class="at">p_reads_herpes =</span> <span class="fu">sum</span>(p_reads_herpes), </span>
+<span id="cb51-1382"><a href="#cb51-1382" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1383"><a href="#cb51-1383" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb51-1384"><a href="#cb51-1384" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(herpes_species_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb51-1385"><a href="#cb51-1385" aria-hidden="true" tabindex="-1"></a>herpes_species_counts_display <span class="ot">&lt;-</span> herpes_species_counts_major <span class="sc">%&gt;%</span></span>
+<span id="cb51-1386"><a href="#cb51-1386" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_herpes, <span class="at">classification =</span> name_display)</span>
+<span id="cb51-1387"><a href="#cb51-1387" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1388"><a href="#cb51-1388" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1389"><a href="#cb51-1389" aria-hidden="true" tabindex="-1"></a>g_herpes_species <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-1390"><a href="#cb51-1390" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>herpes_species_counts_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-1391"><a href="#cb51-1391" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% herpesviridae Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb51-1392"><a href="#cb51-1392" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-1393"><a href="#cb51-1393" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-1394"><a href="#cb51-1394" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral species"</span>) <span class="sc">+</span></span>
+<span id="cb51-1395"><a href="#cb51-1395" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"Species composition of Herpesviridae reads"</span>) <span class="sc">+</span></span>
+<span id="cb51-1396"><a href="#cb51-1396" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
+<span id="cb51-1397"><a href="#cb51-1397" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="fl">1.4</span>), <span class="at">hjust=</span><span class="dv">0</span>, <span class="at">face=</span><span class="st">"plain"</span>))</span>
+<span id="cb51-1398"><a href="#cb51-1398" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1399"><a href="#cb51-1399" aria-hidden="true" tabindex="-1"></a>g_herpes_species</span>
+<span id="cb51-1400"><a href="#cb51-1400" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1401"><a href="#cb51-1401" aria-hidden="true" tabindex="-1"></a><span class="co"># Get most prominent species for text</span></span>
+<span id="cb51-1402"><a href="#cb51-1402" aria-hidden="true" tabindex="-1"></a>herpes_species_collate <span class="ot">&lt;-</span> herpes_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1403"><a href="#cb51-1403" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_tot =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_mean =</span> <span class="fu">mean</span>(p_reads_herpes), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1404"><a href="#cb51-1404" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(n_reads_tot))</span>
+<span id="cb51-1405"><a href="#cb51-1405" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1406"><a href="#cb51-1406" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1407"><a href="#cb51-1407" aria-hidden="true" tabindex="-1"></a>In sharp contrast to the above, my pipeline classifies the great majority of anellovirus reads in all samples into a single species, torque teno virus. Looking online, it looks like there are a lot of "torque teno viruses" within *Anelloviridae* – for example, Wikipedia says that the genus *Alphatorquevirus* contains <span class="sc">\&gt;</span>20 numbered torque teno viruses – so I'm not sure exactly which virus this refers to.</span>
+<span id="cb51-1408"><a href="#cb51-1408" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1411"><a href="#cb51-1411" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1412"><a href="#cb51-1412" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-species-anello</span></span>
+<span id="cb51-1413"><a href="#cb51-1413" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-1414"><a href="#cb51-1414" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1415"><a href="#cb51-1415" aria-hidden="true" tabindex="-1"></a>threshold_major_species <span class="ot">&lt;-</span> <span class="fl">0.1</span></span>
+<span id="cb51-1416"><a href="#cb51-1416" aria-hidden="true" tabindex="-1"></a>taxid_anello <span class="ot">&lt;-</span> viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">"Anelloviridae"</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)</span>
+<span id="cb51-1417"><a href="#cb51-1417" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1418"><a href="#cb51-1418" aria-hidden="true" tabindex="-1"></a><span class="co"># Get set of anelloviridae reads</span></span>
+<span id="cb51-1419"><a href="#cb51-1419" aria-hidden="true" tabindex="-1"></a>anello_samples <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_anello) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1420"><a href="#cb51-1420" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;=</span> <span class="fl">0.1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1421"><a href="#cb51-1421" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(sample)</span>
+<span id="cb51-1422"><a href="#cb51-1422" aria-hidden="true" tabindex="-1"></a>anello_ids <span class="ot">&lt;-</span> hv_reads_family <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1423"><a href="#cb51-1423" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_anello, sample <span class="sc">%in%</span> anello_samples) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1424"><a href="#cb51-1424" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(seq_id)</span>
+<span id="cb51-1425"><a href="#cb51-1425" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1426"><a href="#cb51-1426" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each anelloviridae species</span></span>
+<span id="cb51-1427"><a href="#cb51-1427" aria-hidden="true" tabindex="-1"></a>anello_species_counts <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span></span>
+<span id="cb51-1428"><a href="#cb51-1428" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(seq_id <span class="sc">%in%</span> anello_ids) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1429"><a href="#cb51-1429" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1430"><a href="#cb51-1430" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1431"><a href="#cb51-1431" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1432"><a href="#cb51-1432" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_anello =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb51-1433"><a href="#cb51-1433" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1434"><a href="#cb51-1434" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb51-1435"><a href="#cb51-1435" aria-hidden="true" tabindex="-1"></a>anello_species_major_tab <span class="ot">&lt;-</span> anello_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1436"><a href="#cb51-1436" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_anello <span class="sc">==</span> <span class="fu">max</span>(p_reads_anello)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1437"><a href="#cb51-1437" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1438"><a href="#cb51-1438" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_anello)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1439"><a href="#cb51-1439" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_anello <span class="sc">&gt;</span> threshold_major_species)</span>
+<span id="cb51-1440"><a href="#cb51-1440" aria-hidden="true" tabindex="-1"></a>anello_species_counts_major <span class="ot">&lt;-</span> anello_species_counts <span class="sc">%&gt;%</span></span>
+<span id="cb51-1441"><a href="#cb51-1441" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> anello_species_major_tab<span class="sc">$</span>name, </span>
+<span id="cb51-1442"><a href="#cb51-1442" aria-hidden="true" tabindex="-1"></a>                               name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1443"><a href="#cb51-1443" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1444"><a href="#cb51-1444" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_anello =</span> <span class="fu">sum</span>(n_reads_hv),</span>
+<span id="cb51-1445"><a href="#cb51-1445" aria-hidden="true" tabindex="-1"></a>            <span class="at">p_reads_anello =</span> <span class="fu">sum</span>(p_reads_anello), </span>
+<span id="cb51-1446"><a href="#cb51-1446" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1447"><a href="#cb51-1447" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb51-1448"><a href="#cb51-1448" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(anello_species_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb51-1449"><a href="#cb51-1449" aria-hidden="true" tabindex="-1"></a>anello_species_counts_display <span class="ot">&lt;-</span> anello_species_counts_major <span class="sc">%&gt;%</span></span>
+<span id="cb51-1450"><a href="#cb51-1450" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_anello, <span class="at">classification =</span> name_display)</span>
+<span id="cb51-1451"><a href="#cb51-1451" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1452"><a href="#cb51-1452" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1453"><a href="#cb51-1453" aria-hidden="true" tabindex="-1"></a>g_anello_species <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-1454"><a href="#cb51-1454" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>anello_species_counts_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-1455"><a href="#cb51-1455" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Anelloviridae Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb51-1456"><a href="#cb51-1456" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-1457"><a href="#cb51-1457" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-1458"><a href="#cb51-1458" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral species"</span>) <span class="sc">+</span></span>
+<span id="cb51-1459"><a href="#cb51-1459" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"Species composition of Anelloviridae reads"</span>) <span class="sc">+</span></span>
+<span id="cb51-1460"><a href="#cb51-1460" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">4</span>)) <span class="sc">+</span></span>
+<span id="cb51-1461"><a href="#cb51-1461" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="fl">1.4</span>), <span class="at">hjust=</span><span class="dv">0</span>, <span class="at">face=</span><span class="st">"plain"</span>))</span>
+<span id="cb51-1462"><a href="#cb51-1462" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1463"><a href="#cb51-1463" aria-hidden="true" tabindex="-1"></a>g_anello_species</span>
+<span id="cb51-1464"><a href="#cb51-1464" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1465"><a href="#cb51-1465" aria-hidden="true" tabindex="-1"></a><span class="co"># Get most prominent species for text</span></span>
+<span id="cb51-1466"><a href="#cb51-1466" aria-hidden="true" tabindex="-1"></a>anello_species_collate <span class="ot">&lt;-</span> anello_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1467"><a href="#cb51-1467" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_tot =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_mean =</span> <span class="fu">mean</span>(p_reads_anello), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1468"><a href="#cb51-1468" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(n_reads_tot))</span>
+<span id="cb51-1469"><a href="#cb51-1469" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1470"><a href="#cb51-1470" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1471"><a href="#cb51-1471" aria-hidden="true" tabindex="-1"></a>Polyomaviruses are intermediate; most viruses are dominated by a single species, *Alphapolyomavirus quintihominis*, but several other viruses in the family are also present.</span>
+<span id="cb51-1472"><a href="#cb51-1472" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1475"><a href="#cb51-1475" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1476"><a href="#cb51-1476" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-species-polyoma</span></span>
+<span id="cb51-1477"><a href="#cb51-1477" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-1478"><a href="#cb51-1478" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 9</span></span>
+<span id="cb51-1479"><a href="#cb51-1479" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1480"><a href="#cb51-1480" aria-hidden="true" tabindex="-1"></a>threshold_major_species <span class="ot">&lt;-</span> <span class="fl">0.1</span></span>
+<span id="cb51-1481"><a href="#cb51-1481" aria-hidden="true" tabindex="-1"></a>taxid_polyoma <span class="ot">&lt;-</span> viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">"Polyomaviridae"</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)</span>
+<span id="cb51-1482"><a href="#cb51-1482" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1483"><a href="#cb51-1483" aria-hidden="true" tabindex="-1"></a><span class="co"># Get set of polyomaviridae reads</span></span>
+<span id="cb51-1484"><a href="#cb51-1484" aria-hidden="true" tabindex="-1"></a><span class="co"># Get set of polyomaviridae reads</span></span>
+<span id="cb51-1485"><a href="#cb51-1485" aria-hidden="true" tabindex="-1"></a>polyoma_samples <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_polyoma) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1486"><a href="#cb51-1486" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;=</span> <span class="fl">0.1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1487"><a href="#cb51-1487" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(sample)</span>
+<span id="cb51-1488"><a href="#cb51-1488" aria-hidden="true" tabindex="-1"></a>polyoma_ids <span class="ot">&lt;-</span> hv_reads_family <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1489"><a href="#cb51-1489" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_polyoma, sample <span class="sc">%in%</span> polyoma_samples) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1490"><a href="#cb51-1490" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(seq_id)</span>
+<span id="cb51-1491"><a href="#cb51-1491" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1492"><a href="#cb51-1492" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each polyomaviridae species</span></span>
+<span id="cb51-1493"><a href="#cb51-1493" aria-hidden="true" tabindex="-1"></a>polyoma_species_counts <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span></span>
+<span id="cb51-1494"><a href="#cb51-1494" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(seq_id <span class="sc">%in%</span> polyoma_ids) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1495"><a href="#cb51-1495" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1496"><a href="#cb51-1496" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1497"><a href="#cb51-1497" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1498"><a href="#cb51-1498" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_polyoma =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb51-1499"><a href="#cb51-1499" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1500"><a href="#cb51-1500" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb51-1501"><a href="#cb51-1501" aria-hidden="true" tabindex="-1"></a>polyoma_species_major_tab <span class="ot">&lt;-</span> polyoma_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1502"><a href="#cb51-1502" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_polyoma <span class="sc">==</span> <span class="fu">max</span>(p_reads_polyoma)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1503"><a href="#cb51-1503" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1504"><a href="#cb51-1504" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_polyoma)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1505"><a href="#cb51-1505" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_polyoma <span class="sc">&gt;</span> threshold_major_species)</span>
+<span id="cb51-1506"><a href="#cb51-1506" aria-hidden="true" tabindex="-1"></a>polyoma_species_counts_major <span class="ot">&lt;-</span> polyoma_species_counts <span class="sc">%&gt;%</span></span>
+<span id="cb51-1507"><a href="#cb51-1507" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> polyoma_species_major_tab<span class="sc">$</span>name, </span>
+<span id="cb51-1508"><a href="#cb51-1508" aria-hidden="true" tabindex="-1"></a>                               name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1509"><a href="#cb51-1509" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1510"><a href="#cb51-1510" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_polyoma =</span> <span class="fu">sum</span>(n_reads_hv),</span>
+<span id="cb51-1511"><a href="#cb51-1511" aria-hidden="true" tabindex="-1"></a>            <span class="at">p_reads_polyoma =</span> <span class="fu">sum</span>(p_reads_polyoma), </span>
+<span id="cb51-1512"><a href="#cb51-1512" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1513"><a href="#cb51-1513" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb51-1514"><a href="#cb51-1514" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(polyoma_species_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb51-1515"><a href="#cb51-1515" aria-hidden="true" tabindex="-1"></a>polyoma_species_counts_display <span class="ot">&lt;-</span> polyoma_species_counts_major <span class="sc">%&gt;%</span></span>
+<span id="cb51-1516"><a href="#cb51-1516" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_polyoma, <span class="at">classification =</span> name_display)</span>
+<span id="cb51-1517"><a href="#cb51-1517" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1518"><a href="#cb51-1518" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1519"><a href="#cb51-1519" aria-hidden="true" tabindex="-1"></a>g_polyoma_species <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-1520"><a href="#cb51-1520" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>polyoma_species_counts_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-1521"><a href="#cb51-1521" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Polyomaviridae Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb51-1522"><a href="#cb51-1522" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-1523"><a href="#cb51-1523" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-1524"><a href="#cb51-1524" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral species"</span>) <span class="sc">+</span></span>
+<span id="cb51-1525"><a href="#cb51-1525" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"Species composition of Polyomaviridae reads"</span>) <span class="sc">+</span></span>
+<span id="cb51-1526"><a href="#cb51-1526" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">2</span>)) <span class="sc">+</span></span>
+<span id="cb51-1527"><a href="#cb51-1527" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="fl">1.4</span>), <span class="at">hjust=</span><span class="dv">0</span>, <span class="at">face=</span><span class="st">"plain"</span>))</span>
+<span id="cb51-1528"><a href="#cb51-1528" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1529"><a href="#cb51-1529" aria-hidden="true" tabindex="-1"></a>g_polyoma_species</span>
+<span id="cb51-1530"><a href="#cb51-1530" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1531"><a href="#cb51-1531" aria-hidden="true" tabindex="-1"></a><span class="co"># Get most prominent species for text</span></span>
+<span id="cb51-1532"><a href="#cb51-1532" aria-hidden="true" tabindex="-1"></a>polyoma_species_collate <span class="ot">&lt;-</span> polyoma_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1533"><a href="#cb51-1533" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_tot =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_mean =</span> <span class="fu">mean</span>(p_reads_polyoma), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1534"><a href="#cb51-1534" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(n_reads_tot))</span>
+<span id="cb51-1535"><a href="#cb51-1535" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1536"><a href="#cb51-1536" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1537"><a href="#cb51-1537" aria-hidden="true" tabindex="-1"></a>Finally, poxvirus reads in most samples are dominated by molluscum contagiosum virus (which I expect to be real), followed by Orf virus (which I expect to be fake). These expectations are borne out by BLAST alignments (below).</span>
+<span id="cb51-1538"><a href="#cb51-1538" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1541"><a href="#cb51-1541" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1542"><a href="#cb51-1542" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-species-pox</span></span>
+<span id="cb51-1543"><a href="#cb51-1543" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb51-1544"><a href="#cb51-1544" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1545"><a href="#cb51-1545" aria-hidden="true" tabindex="-1"></a>threshold_major_species <span class="ot">&lt;-</span> <span class="fl">0.1</span></span>
+<span id="cb51-1546"><a href="#cb51-1546" aria-hidden="true" tabindex="-1"></a>taxid_pox <span class="ot">&lt;-</span> viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">"Poxviridae"</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)</span>
+<span id="cb51-1547"><a href="#cb51-1547" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1548"><a href="#cb51-1548" aria-hidden="true" tabindex="-1"></a><span class="co"># Get set of poxviridae reads</span></span>
+<span id="cb51-1549"><a href="#cb51-1549" aria-hidden="true" tabindex="-1"></a><span class="co"># Get set of poxviridae reads</span></span>
+<span id="cb51-1550"><a href="#cb51-1550" aria-hidden="true" tabindex="-1"></a>pox_samples <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_pox) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1551"><a href="#cb51-1551" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;=</span> <span class="fl">0.1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1552"><a href="#cb51-1552" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(sample)</span>
+<span id="cb51-1553"><a href="#cb51-1553" aria-hidden="true" tabindex="-1"></a>pox_ids <span class="ot">&lt;-</span> hv_reads_family <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1554"><a href="#cb51-1554" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">==</span> taxid_pox, sample <span class="sc">%in%</span> pox_samples) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1555"><a href="#cb51-1555" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>(seq_id)</span>
+<span id="cb51-1556"><a href="#cb51-1556" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1557"><a href="#cb51-1557" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each poxviridae species</span></span>
+<span id="cb51-1558"><a href="#cb51-1558" aria-hidden="true" tabindex="-1"></a>pox_species_counts <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span></span>
+<span id="cb51-1559"><a href="#cb51-1559" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(seq_id <span class="sc">%in%</span> pox_ids) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1560"><a href="#cb51-1560" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1561"><a href="#cb51-1561" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1562"><a href="#cb51-1562" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1563"><a href="#cb51-1563" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_pox =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb51-1564"><a href="#cb51-1564" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1565"><a href="#cb51-1565" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb51-1566"><a href="#cb51-1566" aria-hidden="true" tabindex="-1"></a>pox_species_major_tab <span class="ot">&lt;-</span> pox_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1567"><a href="#cb51-1567" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_pox <span class="sc">==</span> <span class="fu">max</span>(p_reads_pox)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1568"><a href="#cb51-1568" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1569"><a href="#cb51-1569" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_pox)) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1570"><a href="#cb51-1570" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_pox <span class="sc">&gt;</span> threshold_major_species)</span>
+<span id="cb51-1571"><a href="#cb51-1571" aria-hidden="true" tabindex="-1"></a>pox_species_counts_major <span class="ot">&lt;-</span> pox_species_counts <span class="sc">%&gt;%</span></span>
+<span id="cb51-1572"><a href="#cb51-1572" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> pox_species_major_tab<span class="sc">$</span>name, </span>
+<span id="cb51-1573"><a href="#cb51-1573" aria-hidden="true" tabindex="-1"></a>                               name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1574"><a href="#cb51-1574" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1575"><a href="#cb51-1575" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_pox =</span> <span class="fu">sum</span>(n_reads_hv),</span>
+<span id="cb51-1576"><a href="#cb51-1576" aria-hidden="true" tabindex="-1"></a>            <span class="at">p_reads_pox =</span> <span class="fu">sum</span>(p_reads_pox), </span>
+<span id="cb51-1577"><a href="#cb51-1577" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1578"><a href="#cb51-1578" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb51-1579"><a href="#cb51-1579" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(pox_species_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb51-1580"><a href="#cb51-1580" aria-hidden="true" tabindex="-1"></a>pox_species_counts_display <span class="ot">&lt;-</span> pox_species_counts_major <span class="sc">%&gt;%</span></span>
+<span id="cb51-1581"><a href="#cb51-1581" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_pox, <span class="at">classification =</span> name_display)</span>
+<span id="cb51-1582"><a href="#cb51-1582" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1583"><a href="#cb51-1583" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1584"><a href="#cb51-1584" aria-hidden="true" tabindex="-1"></a>g_pox_species <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb51-1585"><a href="#cb51-1585" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>pox_species_counts_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb51-1586"><a href="#cb51-1586" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Poxviridae Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb51-1587"><a href="#cb51-1587" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb51-1588"><a href="#cb51-1588" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb51-1589"><a href="#cb51-1589" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral species"</span>) <span class="sc">+</span></span>
+<span id="cb51-1590"><a href="#cb51-1590" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"Species composition of Poxviridae reads"</span>) <span class="sc">+</span></span>
+<span id="cb51-1591"><a href="#cb51-1591" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">4</span>)) <span class="sc">+</span></span>
+<span id="cb51-1592"><a href="#cb51-1592" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="fl">1.4</span>), <span class="at">hjust=</span><span class="dv">0</span>, <span class="at">face=</span><span class="st">"plain"</span>))</span>
+<span id="cb51-1593"><a href="#cb51-1593" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1594"><a href="#cb51-1594" aria-hidden="true" tabindex="-1"></a>g_pox_species</span>
+<span id="cb51-1595"><a href="#cb51-1595" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1596"><a href="#cb51-1596" aria-hidden="true" tabindex="-1"></a><span class="co"># Get most prominent species for text</span></span>
+<span id="cb51-1597"><a href="#cb51-1597" aria-hidden="true" tabindex="-1"></a>pox_species_collate <span class="ot">&lt;-</span> pox_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1598"><a href="#cb51-1598" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_tot =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_mean =</span> <span class="fu">mean</span>(p_reads_pox), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1599"><a href="#cb51-1599" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(n_reads_tot))</span>
+<span id="cb51-1600"><a href="#cb51-1600" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1601"><a href="#cb51-1601" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1604"><a href="#cb51-1604" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1605"><a href="#cb51-1605" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-blast-hits</span></span>
+<span id="cb51-1606"><a href="#cb51-1606" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 6</span></span>
+<span id="cb51-1607"><a href="#cb51-1607" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1608"><a href="#cb51-1608" aria-hidden="true" tabindex="-1"></a><span class="co"># Configure</span></span>
+<span id="cb51-1609"><a href="#cb51-1609" aria-hidden="true" tabindex="-1"></a>ref_taxids_hv <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">10279</span>, <span class="dv">10258</span>)</span>
+<span id="cb51-1610"><a href="#cb51-1610" aria-hidden="true" tabindex="-1"></a>ref_names_hv <span class="ot">&lt;-</span> <span class="fu">sapply</span>(ref_taxids_hv, <span class="cf">function</span>(x) viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> x) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name) <span class="sc">%&gt;%</span> first)</span>
+<span id="cb51-1611"><a href="#cb51-1611" aria-hidden="true" tabindex="-1"></a>p_threshold <span class="ot">&lt;-</span> <span class="fl">0.1</span></span>
+<span id="cb51-1612"><a href="#cb51-1612" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1613"><a href="#cb51-1613" aria-hidden="true" tabindex="-1"></a><span class="co"># Get taxon names</span></span>
+<span id="cb51-1614"><a href="#cb51-1614" aria-hidden="true" tabindex="-1"></a>tax_names_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir_base, <span class="st">"taxid-names.tsv.gz"</span>)</span>
+<span id="cb51-1615"><a href="#cb51-1615" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(tax_names_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb51-1616"><a href="#cb51-1616" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1617"><a href="#cb51-1617" aria-hidden="true" tabindex="-1"></a><span class="co"># Add missing names</span></span>
+<span id="cb51-1618"><a href="#cb51-1618" aria-hidden="true" tabindex="-1"></a>tax_names_new <span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>staxid, <span class="sc">~</span>name,</span>
+<span id="cb51-1619"><a href="#cb51-1619" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3050295</span>, <span class="st">"Cytomegalovirus humanbeta5"</span>,</span>
+<span id="cb51-1620"><a href="#cb51-1620" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">459231</span>, <span class="st">"FLAG-tagging vector pFLAG97-TSR"</span>,</span>
+<span id="cb51-1621"><a href="#cb51-1621" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">257877</span>, <span class="st">"Macaca thibetana thibetana"</span>,</span>
+<span id="cb51-1622"><a href="#cb51-1622" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">256321</span>, <span class="st">"Lentiviral transfer vector pHsCXW"</span>,</span>
+<span id="cb51-1623"><a href="#cb51-1623" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">419242</span>, <span class="st">"Shuttle vector pLvCmvMYOCDHA"</span>,</span>
+<span id="cb51-1624"><a href="#cb51-1624" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">419243</span>, <span class="st">"Shuttle vector pLvCmvLacZ"</span>,</span>
+<span id="cb51-1625"><a href="#cb51-1625" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">421868</span>, <span class="st">"Cloning vector pLvCmvLacZ.Gfp"</span>,</span>
+<span id="cb51-1626"><a href="#cb51-1626" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">421869</span>, <span class="st">"Cloning vector pLvCmvMyocardin.Gfp"</span>,</span>
+<span id="cb51-1627"><a href="#cb51-1627" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">426303</span>, <span class="st">"Lentiviral vector pNL-GFP-RRE(SA)"</span>,</span>
+<span id="cb51-1628"><a href="#cb51-1628" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">436015</span>, <span class="st">"Lentiviral transfer vector pFTMGW"</span>,</span>
+<span id="cb51-1629"><a href="#cb51-1629" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">454257</span>, <span class="st">"Shuttle vector pLvCmvMYOCD2aHA"</span>,</span>
+<span id="cb51-1630"><a href="#cb51-1630" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">476184</span>, <span class="st">"Shuttle vector pLV.mMyoD::ERT2.eGFP"</span>,</span>
+<span id="cb51-1631"><a href="#cb51-1631" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">476185</span>, <span class="st">"Shuttle vector pLV.hMyoD.eGFP"</span>,</span>
+<span id="cb51-1632"><a href="#cb51-1632" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">591936</span>, <span class="st">"Piliocolobus tephrosceles"</span>,</span>
+<span id="cb51-1633"><a href="#cb51-1633" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">627481</span>, <span class="st">"Lentiviral transfer vector pFTM3GW"</span>,</span>
+<span id="cb51-1634"><a href="#cb51-1634" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">680261</span>, <span class="st">"Self-inactivating lentivirus vector pLV.C-EF1a.cyt-bGal.dCpG"</span>,</span>
+<span id="cb51-1635"><a href="#cb51-1635" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">2952778</span>, <span class="st">"Expression vector pLV[Exp]-EGFP:T2A:Puro-EF1A"</span>,</span>
+<span id="cb51-1636"><a href="#cb51-1636" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3022699</span>, <span class="st">"Vector PAS_122122"</span>,</span>
+<span id="cb51-1637"><a href="#cb51-1637" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3025913</span>, <span class="st">"Vector pSIN-WP-mPGK-GDNF"</span>,</span>
+<span id="cb51-1638"><a href="#cb51-1638" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3105863</span>, <span class="st">"Vector pLKO.1-ZsGreen1"</span>,</span>
+<span id="cb51-1639"><a href="#cb51-1639" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3105864</span>, <span class="st">"Vector pLKO.1-ZsGreen1 mouse Wfs1 shRNA"</span>,</span>
+<span id="cb51-1640"><a href="#cb51-1640" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3108001</span>, <span class="st">"Cloning vector pLVSIN-CMV_Neo_v4.0"</span>,</span>
+<span id="cb51-1641"><a href="#cb51-1641" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3109234</span>, <span class="st">"Vector pTwist+Kan+High"</span>,</span>
+<span id="cb51-1642"><a href="#cb51-1642" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3117662</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;P301L"</span>,</span>
+<span id="cb51-1643"><a href="#cb51-1643" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3117663</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;P301L:T2A:mRuby3"</span>,</span>
+<span id="cb51-1644"><a href="#cb51-1644" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3117664</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;hMAPT[NM_005910.6](ns):T2A:mRuby3"</span>,</span>
+<span id="cb51-1645"><a href="#cb51-1645" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3117665</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;mRuby3"</span>,</span>
+<span id="cb51-1646"><a href="#cb51-1646" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3117666</span>, <span class="st">"Cloning vector pLV[Exp]-CBA&gt;mRuby3/NFAT3 fusion protein"</span>,</span>
+<span id="cb51-1647"><a href="#cb51-1647" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3117667</span>, <span class="st">"Cloning vector pLV[Exp]-Neo-mPGK&gt;{EGFP-hSEPT6}"</span>,</span>
+<span id="cb51-1648"><a href="#cb51-1648" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">438045</span>, <span class="st">"Xenotropic MuLV-related virus"</span>,</span>
+<span id="cb51-1649"><a href="#cb51-1649" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">447135</span>, <span class="st">"Myodes glareolus"</span>,</span>
+<span id="cb51-1650"><a href="#cb51-1650" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">590745</span>, <span class="st">"Mus musculus mobilized endogenous polytropic provirus"</span>,</span>
+<span id="cb51-1651"><a href="#cb51-1651" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">181858</span>, <span class="st">"Murine AIDS virus-related provirus"</span>,</span>
+<span id="cb51-1652"><a href="#cb51-1652" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">356663</span>, <span class="st">"Xenotropic MuLV-related virus VP35"</span>,</span>
+<span id="cb51-1653"><a href="#cb51-1653" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">356664</span>, <span class="st">"Xenotropic MuLV-related virus VP42"</span>,</span>
+<span id="cb51-1654"><a href="#cb51-1654" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">373193</span>, <span class="st">"Xenotropic MuLV-related virus VP62"</span>,</span>
+<span id="cb51-1655"><a href="#cb51-1655" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">286419</span>, <span class="st">"Canis lupus dingo"</span>,</span>
+<span id="cb51-1656"><a href="#cb51-1656" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">415978</span>, <span class="st">"Sus scrofa scrofa"</span>,</span>
+<span id="cb51-1657"><a href="#cb51-1657" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">494514</span>, <span class="st">"Vulpes lagopus"</span>,</span>
+<span id="cb51-1658"><a href="#cb51-1658" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3082113</span>, <span class="st">"Rangifer tarandus platyrhynchus"</span>,</span>
+<span id="cb51-1659"><a href="#cb51-1659" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3119969</span>, <span class="st">"Bubalus kerabau"</span>)</span>
+<span id="cb51-1660"><a href="#cb51-1660" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(tax_names, tax_names_new)</span>
+<span id="cb51-1661"><a href="#cb51-1661" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1662"><a href="#cb51-1662" aria-hidden="true" tabindex="-1"></a><span class="co"># Get matches</span></span>
+<span id="cb51-1663"><a href="#cb51-1663" aria-hidden="true" tabindex="-1"></a>hv_blast_staxids <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">%in%</span> ref_taxids_hv) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1664"><a href="#cb51-1664" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(taxid) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">n_seq =</span> <span class="fu">n</span>()) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1665"><a href="#cb51-1665" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(blast_results_paired, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1666"><a href="#cb51-1666" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">staxid =</span> <span class="fu">as.integer</span>(staxid)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1667"><a href="#cb51-1667" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">sname=</span>name), <span class="at">by=</span><span class="st">"staxid"</span>)</span>
+<span id="cb51-1668"><a href="#cb51-1668" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1669"><a href="#cb51-1669" aria-hidden="true" tabindex="-1"></a><span class="co"># Count matches</span></span>
+<span id="cb51-1670"><a href="#cb51-1670" aria-hidden="true" tabindex="-1"></a>hv_blast_counts <span class="ot">&lt;-</span> hv_blast_staxids <span class="sc">%&gt;%</span></span>
+<span id="cb51-1671"><a href="#cb51-1671" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(taxid, name, staxid, sname, n_seq) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1672"><a href="#cb51-1672" aria-hidden="true" tabindex="-1"></a>  count <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p=</span>n<span class="sc">/</span>n_seq)</span>
+<span id="cb51-1673"><a href="#cb51-1673" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1674"><a href="#cb51-1674" aria-hidden="true" tabindex="-1"></a><span class="co"># Subset to major matches</span></span>
+<span id="cb51-1675"><a href="#cb51-1675" aria-hidden="true" tabindex="-1"></a>hv_blast_counts_major <span class="ot">&lt;-</span> hv_blast_counts <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1676"><a href="#cb51-1676" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(n<span class="sc">&gt;</span><span class="dv">1</span>, p<span class="sc">&gt;</span>p_threshold, <span class="sc">!</span><span class="fu">is.na</span>(staxid)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1677"><a href="#cb51-1677" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p)) <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1678"><a href="#cb51-1678" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">&lt;=</span> <span class="dv">25</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1679"><a href="#cb51-1679" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">==</span> ref_names_hv[<span class="dv">1</span>], <span class="st">"MCV"</span>, name))</span>
+<span id="cb51-1680"><a href="#cb51-1680" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1681"><a href="#cb51-1681" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1682"><a href="#cb51-1682" aria-hidden="true" tabindex="-1"></a>g_hv_blast <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(hv_blast_counts_major, <span class="at">mapping=</span><span class="fu">aes</span>(<span class="at">x=</span>p, <span class="at">y=</span>sname)) <span class="sc">+</span></span>
+<span id="cb51-1683"><a href="#cb51-1683" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>() <span class="sc">+</span></span>
+<span id="cb51-1684"><a href="#cb51-1684" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(name_display<span class="sc">~</span>., <span class="at">scales=</span><span class="st">"free_y"</span>, <span class="at">space=</span><span class="st">"free_y"</span>) <span class="sc">+</span></span>
+<span id="cb51-1685"><a href="#cb51-1685" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"% mapped reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), </span>
+<span id="cb51-1686"><a href="#cb51-1686" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb51-1687"><a href="#cb51-1687" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.title.y =</span> <span class="fu">element_blank</span>())</span>
+<span id="cb51-1688"><a href="#cb51-1688" aria-hidden="true" tabindex="-1"></a>g_hv_blast</span>
+<span id="cb51-1689"><a href="#cb51-1689" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1690"><a href="#cb51-1690" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1691"><a href="#cb51-1691" aria-hidden="true" tabindex="-1"></a>Finally, here again are the overall relative abundances of the specific viral genera I picked out manually in my last entry:</span>
+<span id="cb51-1692"><a href="#cb51-1692" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1695"><a href="#cb51-1695" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb51-1696"><a href="#cb51-1696" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 5</span></span>
+<span id="cb51-1697"><a href="#cb51-1697" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: ra-genera</span></span>
+<span id="cb51-1698"><a href="#cb51-1698" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1699"><a href="#cb51-1699" aria-hidden="true" tabindex="-1"></a><span class="co"># Define reference genera</span></span>
+<span id="cb51-1700"><a href="#cb51-1700" aria-hidden="true" tabindex="-1"></a>path_genera_rna <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"Mamastrovirus"</span>, <span class="st">"Enterovirus"</span>, <span class="st">"Salivirus"</span>, <span class="st">"Kobuvirus"</span>, <span class="st">"Norovirus"</span>, <span class="st">"Sapovirus"</span>, <span class="st">"Rotavirus"</span>, <span class="st">"Alphacoronavirus"</span>, <span class="st">"Betacoronavirus"</span>, <span class="st">"Alphainfluenzavirus"</span>, <span class="st">"Betainfluenzavirus"</span>, <span class="st">"Lentivirus"</span>)</span>
+<span id="cb51-1701"><a href="#cb51-1701" aria-hidden="true" tabindex="-1"></a>path_genera_dna <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"Mastadenovirus"</span>, <span class="st">"Alphapolyomavirus"</span>, <span class="st">"Betapolyomavirus"</span>, <span class="st">"Alphapapillomavirus"</span>, <span class="st">"Betapapillomavirus"</span>, <span class="st">"Gammapapillomavirus"</span>, <span class="st">"Orthopoxvirus"</span>, <span class="st">"Simplexvirus"</span>,</span>
+<span id="cb51-1702"><a href="#cb51-1702" aria-hidden="true" tabindex="-1"></a>                     <span class="st">"Lymphocryptovirus"</span>, <span class="st">"Cytomegalovirus"</span>, <span class="st">"Dependoparvovirus"</span>)</span>
+<span id="cb51-1703"><a href="#cb51-1703" aria-hidden="true" tabindex="-1"></a>path_genera <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(<span class="fu">tibble</span>(<span class="at">name=</span>path_genera_rna, <span class="at">genome_type=</span><span class="st">"RNA genome"</span>),</span>
+<span id="cb51-1704"><a href="#cb51-1704" aria-hidden="true" tabindex="-1"></a>                         <span class="fu">tibble</span>(<span class="at">name=</span>path_genera_dna, <span class="at">genome_type=</span><span class="st">"DNA genome"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1705"><a href="#cb51-1705" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(viral_taxa, <span class="at">by=</span><span class="st">"name"</span>)</span>
+<span id="cb51-1706"><a href="#cb51-1706" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1707"><a href="#cb51-1707" aria-hidden="true" tabindex="-1"></a><span class="co"># Count in each sample</span></span>
+<span id="cb51-1708"><a href="#cb51-1708" aria-hidden="true" tabindex="-1"></a>n_path_genera <span class="ot">&lt;-</span> hv_reads_genus <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1709"><a href="#cb51-1709" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date_alias, city, name, taxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1710"><a href="#cb51-1710" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name=</span><span class="st">"n_reads_viral"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1711"><a href="#cb51-1711" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(path_genera, <span class="at">by=</span><span class="fu">c</span>(<span class="st">"name"</span>, <span class="st">"taxid"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1712"><a href="#cb51-1712" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(read_counts_raw, <span class="at">by=</span><span class="fu">c</span>(<span class="st">"sample"</span>, <span class="st">"date_alias"</span>, <span class="st">"city"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1713"><a href="#cb51-1713" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_viral =</span> n_reads_viral<span class="sc">/</span>n_reads_raw)</span>
+<span id="cb51-1714"><a href="#cb51-1714" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1715"><a href="#cb51-1715" aria-hidden="true" tabindex="-1"></a><span class="co"># Pivot out and back to add zero lines</span></span>
+<span id="cb51-1716"><a href="#cb51-1716" aria-hidden="true" tabindex="-1"></a>n_path_genera_out <span class="ot">&lt;-</span> n_path_genera <span class="sc">%&gt;%</span> ungroup <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, name, n_reads_viral) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1717"><a href="#cb51-1717" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">names_from=</span><span class="st">"name"</span>, <span class="at">values_from=</span><span class="st">"n_reads_viral"</span>, <span class="at">values_fill=</span><span class="dv">0</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1718"><a href="#cb51-1718" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="sc">-</span>sample, <span class="at">names_to=</span><span class="st">"name"</span>, <span class="at">values_to=</span><span class="st">"n_reads_viral"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1719"><a href="#cb51-1719" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(read_counts_raw, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1720"><a href="#cb51-1720" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(path_genera, <span class="at">by=</span><span class="st">"name"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1721"><a href="#cb51-1721" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_viral =</span> n_reads_viral<span class="sc">/</span>n_reads_raw)</span>
+<span id="cb51-1722"><a href="#cb51-1722" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1723"><a href="#cb51-1723" aria-hidden="true" tabindex="-1"></a><span class="do">## Aggregate across dates</span></span>
+<span id="cb51-1724"><a href="#cb51-1724" aria-hidden="true" tabindex="-1"></a>n_path_genera_stype <span class="ot">&lt;-</span> n_path_genera_out <span class="sc">%&gt;%</span> </span>
+<span id="cb51-1725"><a href="#cb51-1725" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(name, taxid, genome_type, city) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1726"><a href="#cb51-1726" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_raw =</span> <span class="fu">sum</span>(n_reads_raw),</span>
+<span id="cb51-1727"><a href="#cb51-1727" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads_viral =</span> <span class="fu">sum</span>(n_reads_viral), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb51-1728"><a href="#cb51-1728" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample=</span><span class="st">"All samples"</span>, <span class="at">date=</span><span class="st">"All dates"</span>,</span>
+<span id="cb51-1729"><a href="#cb51-1729" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_viral =</span> n_reads_viral<span class="sc">/</span>n_reads_raw,</span>
+<span id="cb51-1730"><a href="#cb51-1730" aria-hidden="true" tabindex="-1"></a>         <span class="at">na_type =</span> <span class="st">"DNA"</span>)</span>
+<span id="cb51-1731"><a href="#cb51-1731" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1732"><a href="#cb51-1732" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb51-1733"><a href="#cb51-1733" aria-hidden="true" tabindex="-1"></a>g_path_genera <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(n_path_genera_stype,</span>
+<span id="cb51-1734"><a href="#cb51-1734" aria-hidden="true" tabindex="-1"></a>                        <span class="fu">aes</span>(<span class="at">y=</span>name, <span class="at">x=</span>p_reads_viral, <span class="at">color=</span>city)) <span class="sc">+</span></span>
+<span id="cb51-1735"><a href="#cb51-1735" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>() <span class="sc">+</span></span>
+<span id="cb51-1736"><a href="#cb51-1736" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_log10</span>(<span class="at">name=</span><span class="st">"Relative abundance"</span>) <span class="sc">+</span></span>
+<span id="cb51-1737"><a href="#cb51-1737" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_city</span>() <span class="sc">+</span></span>
+<span id="cb51-1738"><a href="#cb51-1738" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(genome_type<span class="sc">~</span>., <span class="at">scales=</span><span class="st">"free_y"</span>) <span class="sc">+</span></span>
+<span id="cb51-1739"><a href="#cb51-1739" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.title.y =</span> <span class="fu">element_blank</span>())</span>
+<span id="cb51-1740"><a href="#cb51-1740" aria-hidden="true" tabindex="-1"></a>g_path_genera</span>
+<span id="cb51-1741"><a href="#cb51-1741" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb51-1742"><a href="#cb51-1742" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1743"><a href="#cb51-1743" aria-hidden="true" tabindex="-1"></a><span class="fu"># Conclusion</span></span>
+<span id="cb51-1744"><a href="#cb51-1744" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1745"><a href="#cb51-1745" aria-hidden="true" tabindex="-1"></a>This is the third, largest, and final of this tranche of air-sampling datasets that I've run through this pipeline. Many of the high-level findings were similar to Prussin and Rosario, including high relative abundance of human reads, low total viral reads, an absence of enteric viruses, and high abundance of papillomaviruses among human-infecting viruses.</span>
+<span id="cb51-1746"><a href="#cb51-1746" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb51-1747"><a href="#cb51-1747" aria-hidden="true" tabindex="-1"></a>In the future, I'll do a more in-depth comparative analysis across different datasets to compare the abundance of different viruses. For now, though, there are some major updates to the pipeline I want to make before I do any more public analyses.</span>
+</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div></div></div></div></div>
+</div> <!-- /content -->
+
+
+
+</body></html>
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diff --git a/docs/notebooks/2024-04-12_rosario.html b/docs/notebooks/2024-04-12_rosario.html
index c6719dc..b395e19 100644
--- a/docs/notebooks/2024-04-12_rosario.html
+++ b/docs/notebooks/2024-04-12_rosario.html
@@ -176,7 +176,7 @@
 <!-- margin-sidebar -->
     
 <!-- main -->
-<main class="content quarto-banner-title-block" id="quarto-document-content"><p>Continuing our look at air sampling datasets, we turn to Rosario et al.&nbsp;(2018), another study of air filters, this time from HVAC filters from an undergraduate dorm building at the University of Colorado campus in Boulder. As in <a href="https://data.securebio.org/wills-public-notebook/notebooks/2024-04-12_prussin.html">Prussin</a>, samples were eluted from filters (in this case MERV-8, so less stringent than Prussin’s MERV-14 filters) and underwent both RNA and DNA sequencing – this time on an Illumina MiSeq with 2x250bp reads.</p>
+<main class="content quarto-banner-title-block" id="quarto-document-content"><p>Continuing our look at air sampling datasets, we turn to <a href="https://pubs.acs.org/doi/10.1021/acs.est.7b04203">Rosario et al.&nbsp;(2018)</a>, another study of air filters, this time from HVAC filters from an undergraduate dorm building at the University of Colorado campus in Boulder. As in <a href="https://data.securebio.org/wills-public-notebook/notebooks/2024-04-12_prussin.html">Prussin</a>, samples were eluted from filters (in this case MERV-8, so less stringent than Prussin’s MERV-14 filters) and underwent both RNA and DNA sequencing – this time on an Illumina MiSeq with 2x250bp reads.</p>
 <section id="the-raw-data" class="level1"><h1>The raw data</h1>
 <p>The Rosario dataset comprised sequencing data from 12 individual dormitory rooms at the sampling site, as well as a pooled sample of eight additional rooms and a negative control.</p>
 <div class="cell">
@@ -2018,7 +2018,7 @@
 <span id="cb45-34"><a href="#cb45-34" aria-hidden="true" tabindex="-1"></a>tnl <span class="ot">&lt;-</span> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)</span>
 <span id="cb45-35"><a href="#cb45-35" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb45-36"><a href="#cb45-36" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb45-37"><a href="#cb45-37" aria-hidden="true" tabindex="-1"></a>Continuing our look at air sampling datasets, we turn to Rosario et al. (2018), another study of air filters, this time from HVAC filters from an undergraduate dorm building at the University of Colorado campus in Boulder. As in <span class="co">[</span><span class="ot">Prussin</span><span class="co">](https://data.securebio.org/wills-public-notebook/notebooks/2024-04-12_prussin.html)</span>, samples were eluted from filters (in this case MERV-8, so less stringent than Prussin's MERV-14 filters) and underwent both RNA and DNA sequencing – this time on an Illumina MiSeq with 2x250bp reads.</span>
+<span id="cb45-37"><a href="#cb45-37" aria-hidden="true" tabindex="-1"></a>Continuing our look at air sampling datasets, we turn to <span class="co">[</span><span class="ot">Rosario et al. (2018)</span><span class="co">](https://pubs.acs.org/doi/10.1021/acs.est.7b04203)</span>, another study of air filters, this time from HVAC filters from an undergraduate dorm building at the University of Colorado campus in Boulder. As in <span class="co">[</span><span class="ot">Prussin</span><span class="co">](https://data.securebio.org/wills-public-notebook/notebooks/2024-04-12_prussin.html)</span>, samples were eluted from filters (in this case MERV-8, so less stringent than Prussin's MERV-14 filters) and underwent both RNA and DNA sequencing – this time on an Illumina MiSeq with 2x250bp reads.</span>
 <span id="cb45-38"><a href="#cb45-38" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb45-39"><a href="#cb45-39" aria-hidden="true" tabindex="-1"></a><span class="fu"># The raw data</span></span>
 <span id="cb45-40"><a href="#cb45-40" aria-hidden="true" tabindex="-1"></a></span>
diff --git a/notebooks/2024-04-12_leung.qmd b/notebooks/2024-04-12_leung.qmd
new file mode 100644
index 0000000..e55ffc4
--- /dev/null
+++ b/notebooks/2024-04-12_leung.qmd
@@ -0,0 +1,1673 @@
+---
+title: "Workflow analysis of Leung et al. (2021)"
+subtitle: "Air sampling from a student dorm in Colorado."
+author: "Will Bradshaw"
+date: 2024-04-12
+format:
+  html:
+    code-fold: true
+    code-tools: true
+    code-link: true
+    df-print: paged
+editor: visual
+title-block-banner: black
+draft: true
+---
+
+```{r}
+#| label: load-packages
+#| include: false
+library(tidyverse)
+library(cowplot)
+library(patchwork)
+library(fastqcr)
+library(RColorBrewer)
+source("../scripts/aux_plot-theme.R")
+theme_base <- theme_base + theme(aspect.ratio = NULL)
+theme_rotate <- theme_base + theme(
+    axis.text.x = element_text(hjust = 1, angle = 45),
+)
+theme_kit <- theme_rotate + theme(
+  axis.title.x = element_blank(),
+)
+tnl <- theme(legend.position = "none")
+```
+
+The last in our current run of air sampling datasets is [Leung et al.](https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-021-01044-7) (2021), a study of active air samples collected in public transit systems from six cities (Denver, Hong Kong, London, NYC, Oslo, Stockholm) from June to September 2017.
+
+Samples from Denver originated from their rail and bus system; all other samples originated from metro systems. Collection took place during working days and working hours. Air samples were collected with the SASS 3100 Dry Air Samplers (filtration) for 30 min at a flowrate of 300 L/min using electret microfibrous filters. Filters were stationed at 1.5m above floor level, facing downward (to avoid direct deposition).
+
+This was a DNA-sequencing study, focused on the bacterial microbiome and resistome. Sample processing followed an ideosyncratic protocol, where samples were pelleted and the pellet and supernatant were processed separately before being recombined for NA extraction and sequencing; I don't have a great understanding of how this is expected to affect the viral fraction. Samples were sequenced with Illumina HiSeqX 2x150bp.
+
+# The raw data
+
+In total, the Leung dataset comprised 293 samples:
+
+```{r}
+#| warning: false
+#| label: import-qc-data
+
+# Importing the data is a bit more complicated this time as the samples are split across three pipeline runs
+data_dir_base <- "../data/2024-04-12_leung"
+data_dirs <- paste(data_dir_base, c(1,2,3), sep="/")
+
+# Define geo relationships for filling in
+geo <- tribble(~region, ~country, ~city,
+               "Asia", "Hong Kong", "Hong Kong",
+               "Europe", "Norway", "Oslo",
+               "Europe", "Sweden", "Stockholm",
+               "Europe", "United Kingdom", "London",
+               "North America", "USA", "New York City",
+               "North America", "USA", "Denver")
+
+# Data input paths
+libraries_paths <- file.path(data_dirs, "sample-metadata.csv")
+basic_stats_paths <- file.path(data_dirs, "qc_basic_stats.tsv.gz")
+adapter_stats_paths <- file.path(data_dirs, "qc_adapter_stats.tsv.gz")
+quality_base_stats_paths <- file.path(data_dirs, "qc_quality_base_stats.tsv.gz")
+quality_seq_stats_paths <- file.path(data_dirs, "qc_quality_sequence_stats.tsv.gz")
+
+# Import libraries and extract metadata from sample names
+libraries_raw <- lapply(libraries_paths, read_csv, show_col_types = FALSE) %>%
+  bind_rows
+libraries <- libraries_raw %>%
+  # Fix missing entries
+  mutate(city = ifelse(is.na(city), sub(", .*", "", location), city)) %>%
+  left_join(geo, by="city", suffix = c("", "_new")) %>%
+  mutate(region = ifelse(region == "uncalculated", region_new, region),
+         country = ifelse(country == "uncalculated", country_new, country)) %>%
+  select(-country_new, -region_new) %>%
+  # Add sample aliases
+  arrange(city, date, location) %>%
+  group_by(city, date) %>%
+  mutate(sample_count = row_number(),
+         date_alias = paste(as.character(date), sample_count, sep="_"),
+         sample_alias = paste(city, date_alias, sep="_"))
+
+count_city <- libraries %>% group_by(region, country, city) %>% 
+  count(name="n_samples")
+count_city
+```
+
+```{r}
+#| label: process-qc-data
+
+# Import QC data
+stages <- c("raw_concat", "cleaned", "dedup", "ribo_initial", "ribo_secondary")
+import_basic <- function(paths){
+  lapply(paths, read_tsv, show_col_types = FALSE) %>% bind_rows %>%
+    inner_join(libraries, by="sample") %>% arrange(sample) %>%
+    mutate(stage = factor(stage, levels = stages),
+           sample = fct_inorder(sample))
+}
+import_basic_paired <- function(paths){
+  import_basic(paths) %>% arrange(read_pair) %>% 
+    mutate(read_pair = fct_inorder(as.character(read_pair)))
+}
+basic_stats <- import_basic(basic_stats_paths)
+adapter_stats <- import_basic_paired(adapter_stats_paths)
+quality_base_stats <- import_basic_paired(quality_base_stats_paths)
+quality_seq_stats <- import_basic_paired(quality_seq_stats_paths)
+
+# Filter to raw data
+basic_stats_raw <- basic_stats %>% filter(stage == "raw_concat")
+adapter_stats_raw <- adapter_stats %>% filter(stage == "raw_concat")
+quality_base_stats_raw <- quality_base_stats %>% filter(stage == "raw_concat")
+quality_seq_stats_raw <- quality_seq_stats %>% filter(stage == "raw_concat")
+
+# Get key values for readout
+raw_read_counts <- basic_stats_raw %>% ungroup %>% 
+  summarize(rmin = min(n_read_pairs), rmax=max(n_read_pairs),
+            rmean=mean(n_read_pairs), 
+            rtot = sum(n_read_pairs),
+            btot = sum(n_bases_approx),
+            dmin = min(percent_duplicates), dmax=max(percent_duplicates),
+            dmean=mean(percent_duplicates), .groups = "drop")
+```
+
+These 293 samples yielded 0.39M-7.86M (mean 4.57M) reads per sample, for a total of 1.34B read pairs (402 gigabases of sequence). Read qualities were high at the 5' end but dropped off significantly in some samples, in definite need of cleaning. Adapter levels were high. With the exception of a couple of early samples, inferred duplication levels were low (mean 9.4%).
+
+```{r}
+#| fig-width: 9
+#| warning: false
+#| label: plot-basic-stats
+
+# Prepare data
+basic_stats_raw_metrics <- basic_stats_raw %>%
+  select(sample, city, date,
+         `# Read pairs` = n_read_pairs,
+         `Total base pairs\n(approx)` = n_bases_approx,
+         `% Duplicates\n(FASTQC)` = percent_duplicates) %>%
+  pivot_longer(-(sample:date), names_to = "metric", values_to = "value") %>%
+  mutate(metric = fct_inorder(metric))
+
+# Set up plot templates
+scale_fill_city <- purrr::partial(scale_fill_brewer, palette="Set1",
+                                  name="City")
+scale_x_cdate <- purrr::partial(scale_x_date, name="Collection Date",
+                                date_breaks = "1 month", date_labels = "%Y-%m-%d")
+g_basic <- ggplot(basic_stats_raw_metrics, 
+                  aes(x=date, y=value, fill=city, group=interaction(city,sample))) +
+  geom_col(position = "dodge") +
+  scale_x_cdate() +
+  scale_y_continuous(expand=c(0,0)) +
+  expand_limits(y=c(0,100)) +
+  scale_fill_city() + 
+  facet_grid(metric~., scales = "free", space="free_x", switch="y") +
+  theme_rotate + theme(
+    axis.title.y = element_blank(),
+    strip.text.y = element_text(face="plain")
+  )
+g_basic
+```
+
+```{r}
+#| label: plot-raw-quality
+
+# Set up plotting templates
+scale_color_city <- purrr::partial(scale_color_brewer, palette="Set1",
+                                   name="City")
+g_qual_raw <- ggplot(mapping=aes(color=city, linetype=read_pair, 
+                         group=interaction(sample,read_pair))) + 
+  scale_color_city() + scale_linetype_discrete(name = "Read Pair") +
+  guides(color=guide_legend(nrow=2,byrow=TRUE),
+         linetype = guide_legend(nrow=2,byrow=TRUE)) +
+  theme_base
+
+# Visualize adapters
+g_adapters_raw <- g_qual_raw + 
+  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats_raw) +
+  scale_y_continuous(name="% Adapters", limits=c(0,NA),
+                     breaks = seq(0,100,10), expand=c(0,0)) +
+  scale_x_continuous(name="Position", limits=c(0,NA),
+                     breaks=seq(0,140,20), expand=c(0,0)) +
+  facet_grid(.~adapter)
+g_adapters_raw
+
+# Visualize quality
+g_quality_base_raw <- g_qual_raw +
+  geom_hline(yintercept=25, linetype="dashed", color="red") +
+  geom_hline(yintercept=30, linetype="dashed", color="red") +
+  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats_raw) +
+  scale_y_continuous(name="Mean Phred score", expand=c(0,0), limits=c(10,45)) +
+  scale_x_continuous(name="Position", limits=c(0,NA),
+                     breaks=seq(0,140,20), expand=c(0,0))
+g_quality_base_raw
+
+g_quality_seq_raw <- g_qual_raw +
+  geom_vline(xintercept=25, linetype="dashed", color="red") +
+  geom_vline(xintercept=30, linetype="dashed", color="red") +
+  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats_raw) +
+  scale_x_continuous(name="Mean Phred score", expand=c(0,0)) +
+  scale_y_continuous(name="# Sequences", expand=c(0,0))
+g_quality_seq_raw
+```
+
+# Preprocessing
+
+The average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. Read loss during cleaning was highly variable but averaged 11%, with a further \~7% lost during deduplication and \~0.3% during ribodepletion.
+
+```{r}
+#| label: preproc-table
+n_reads_rel <- basic_stats %>% 
+  select(sample, date, city, stage, 
+         percent_duplicates, n_read_pairs) %>%
+  group_by(sample) %>% arrange(sample, stage) %>%
+  mutate(p_reads_retained = replace_na(n_read_pairs / lag(n_read_pairs), 0),
+         p_reads_lost = 1 - p_reads_retained,
+         p_reads_retained_abs = n_read_pairs / n_read_pairs[1],
+         p_reads_lost_abs = 1-p_reads_retained_abs,
+         p_reads_lost_abs_marginal = replace_na(p_reads_lost_abs - lag(p_reads_lost_abs), 0))
+n_reads_rel_display <- n_reads_rel %>% 
+  rename(Stage=stage, City=city) %>% 
+  group_by(Stage) %>% 
+  summarize(`% Total Reads Lost (Cumulative)` = paste0(round(min(p_reads_lost_abs*100),1), "-", round(max(p_reads_lost_abs*100),1), " (mean ", round(mean(p_reads_lost_abs*100),1), ")"),
+            `% Total Reads Lost (Marginal)` = paste0(round(min(p_reads_lost_abs_marginal*100),1), "-", round(max(p_reads_lost_abs_marginal*100),1), " (mean ", round(mean(p_reads_lost_abs_marginal*100),1), ")"), .groups="drop") %>% 
+  filter(Stage != "raw_concat") %>%
+  mutate(Stage = Stage %>% as.numeric %>% factor(labels=c("Trimming & filtering", "Deduplication", "Initial ribodepletion", "Secondary ribodepletion")))
+n_reads_rel_display
+```
+
+```{r}
+#| label: preproc-figures
+#| warning: false
+#| fig-height: 6
+#| fig-width: 6
+
+g_stage_trace <- ggplot(basic_stats, aes(x=stage, color=city, group=sample)) +
+  scale_color_city() +
+  facet_wrap(~city, scales="free", ncol=3) +
+  theme_kit + theme(legend.position = "none")
+
+# Plot reads over preprocessing
+g_reads_stages <- g_stage_trace +
+  geom_line(aes(y=n_read_pairs)) +
+  scale_y_continuous("# Read pairs", expand=c(0,0), limits=c(0,NA))
+g_reads_stages
+
+# Plot relative read losses during preprocessing
+g_reads_rel <- ggplot(n_reads_rel, aes(x=stage, color=city, group=sample)) +
+  geom_line(aes(y=p_reads_lost_abs_marginal)) +
+  scale_y_continuous("% Total Reads Lost", expand=c(0,0), 
+                     labels = function(x) x*100) +
+  scale_color_city() +
+  facet_wrap(~city, scales="free", ncol=3) +
+  theme_kit + theme(legend.position = "none")
+g_reads_rel
+```
+
+Data cleaning was very successful at removing adapters and improving read qualities:
+
+```{r}
+#| warning: false
+#| label: plot-quality
+#| fig-height: 7
+
+g_qual <- ggplot(mapping=aes(color=city, linetype=read_pair, 
+                         group=interaction(sample,read_pair))) + 
+  scale_color_city() + scale_linetype_discrete(name = "Read Pair") +
+  guides(color=guide_legend(nrow=2,byrow=TRUE),
+         linetype = guide_legend(nrow=2,byrow=TRUE)) +
+  theme_base
+
+# Visualize adapters
+g_adapters <- g_qual + 
+  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats) +
+  scale_y_continuous(name="% Adapters", limits=c(0,20),
+                     breaks = seq(0,50,10), expand=c(0,0)) +
+  scale_x_continuous(name="Position", limits=c(0,NA),
+                     breaks=seq(0,140,20), expand=c(0,0)) +
+  facet_grid(stage~adapter)
+g_adapters
+
+# Visualize quality
+g_quality_base <- g_qual +
+  geom_hline(yintercept=25, linetype="dashed", color="red") +
+  geom_hline(yintercept=30, linetype="dashed", color="red") +
+  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats) +
+  scale_y_continuous(name="Mean Phred score", expand=c(0,0), limits=c(10,45)) +
+  scale_x_continuous(name="Position", limits=c(0,NA),
+                     breaks=seq(0,140,20), expand=c(0,0)) +
+  facet_grid(stage~.)
+g_quality_base
+
+g_quality_seq <- g_qual +
+  geom_vline(xintercept=25, linetype="dashed", color="red") +
+  geom_vline(xintercept=30, linetype="dashed", color="red") +
+  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats) +
+  scale_x_continuous(name="Mean Phred score", expand=c(0,0)) +
+  scale_y_continuous(name="# Sequences", expand=c(0,0)) +
+  facet_grid(stage~.)
+g_quality_seq
+```
+
+According to FASTQC, cleaning + deduplication was very effective at reducing measured duplicate levels, which fell from an average of 9.4% to 1.7% for DNA reads:
+
+```{r}
+#| label: preproc-dedup
+#| fig-height: 6
+#| fig-width: 6
+
+stage_dup <- basic_stats %>% group_by(stage) %>% 
+  summarize(dmin = min(percent_duplicates), dmax=max(percent_duplicates),
+            dmean=mean(percent_duplicates), .groups = "drop")
+
+g_dup_stages <- g_stage_trace +
+  geom_line(aes(y=percent_duplicates)) +
+  scale_y_continuous("% Duplicates", limits=c(0,NA), expand=c(0,0))
+g_dup_stages
+
+g_readlen_stages <- g_stage_trace + geom_line(aes(y=mean_seq_len)) +
+  scale_y_continuous("Mean read length (nt)", expand=c(0,0), limits=c(0,NA))
+g_readlen_stages
+```
+
+# High-level composition
+
+As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:
+
+```{r}
+#| label: prepare-composition
+
+# Import Bracken data
+bracken_paths <- file.path(data_dirs, "bracken_counts.tsv")
+bracken <- lapply(bracken_paths, read_tsv, show_col_types = FALSE) %>% bind_rows
+total_assigned <- bracken %>% group_by(sample) %>% summarize(
+  name = "Total",
+  kraken_assigned_reads = sum(kraken_assigned_reads),
+  added_reads = sum(added_reads),
+  new_est_reads = sum(new_est_reads),
+  fraction_total_reads = sum(fraction_total_reads)
+)
+bracken_spread <- bracken %>% select(name, sample, new_est_reads) %>%
+  mutate(name = tolower(name)) %>%
+  pivot_wider(id_cols = "sample", names_from = "name", 
+              values_from = "new_est_reads")
+
+# Count reads
+read_counts_preproc <- basic_stats %>% 
+  select(sample, date, date_alias, city, stage, n_read_pairs) %>%
+  pivot_wider(id_cols = c("sample", "date", "date_alias", "city"),
+              names_from="stage", values_from="n_read_pairs")
+read_counts <- read_counts_preproc %>%
+  inner_join(total_assigned %>% select(sample, new_est_reads), by = "sample") %>%
+  rename(assigned = new_est_reads) %>%
+  inner_join(bracken_spread, by="sample")
+
+# Assess composition
+read_comp <- transmute(read_counts, sample, date, date_alias, city,
+                       n_filtered = raw_concat-cleaned,
+                       n_duplicate = cleaned-dedup,
+                       n_ribosomal = (dedup-ribo_initial) + (ribo_initial-ribo_secondary),
+                       n_unassigned = ribo_secondary-assigned,
+                       n_bacterial = bacteria,
+                       n_archaeal = archaea,
+                       n_viral = viruses,
+                       n_human = eukaryota)
+read_comp_long <- pivot_longer(read_comp, -(sample:city), 
+                               names_to = "classification",
+                               names_prefix = "n_", values_to = "n_reads") %>%
+  mutate(classification = fct_inorder(str_to_sentence(classification))) %>%
+  group_by(sample) %>% mutate(p_reads = n_reads/sum(n_reads))
+
+# Summarize composition
+read_comp_summ <- read_comp_long %>% 
+  group_by(city, classification) %>%
+  summarize(n_reads = sum(n_reads), .groups = "drop_last") %>%
+  mutate(n_reads = replace_na(n_reads,0),
+    p_reads = n_reads/sum(n_reads),
+    pc_reads = p_reads*100)
+```
+
+```{r}
+#| label: plot-composition-all
+#| fig-height: 7
+#| fig-width: 8
+
+# Prepare plotting templates
+g_comp_base <- ggplot(mapping=aes(x=date_alias, y=p_reads, fill=classification)) +
+  scale_x_discrete(name="Collection Date") +
+  facet_wrap(~city, scales = "free") +
+  theme_kit + theme(axis.text.x = element_blank())
+scale_y_pc_reads <- purrr::partial(scale_y_continuous, name = "% Reads",
+                                   expand = c(0,0), labels = function(y) y*100)
+
+# Plot overall composition
+g_comp <- g_comp_base + geom_col(data = read_comp_long, position = "stack") +
+  scale_y_pc_reads(limits = c(0,1.01), breaks = seq(0,1,0.2)) +
+  scale_fill_brewer(palette = "Set1", name = "Classification")
+g_comp
+
+# Plot composition of minor components
+read_comp_minor <- read_comp_long %>% 
+  filter(classification %in% c("Archaeal", "Viral", "Other"))
+palette_minor <- brewer.pal(9, "Set1")[c(6,7,9)]
+g_comp_minor <- g_comp_base + geom_col(data=read_comp_minor, position = "stack") +
+  scale_y_pc_reads() +
+  scale_fill_manual(values=palette_minor, name = "Classification")
+g_comp_minor
+
+```
+
+```{r}
+#| label: composition-summary
+
+p_reads_summ_group <- read_comp_long %>%
+  mutate(classification = ifelse(classification %in% c("Filtered", "Duplicate", "Unassigned"), "Excluded", as.character(classification)),
+         classification = fct_inorder(classification)) %>%
+  group_by(classification, sample, city) %>%
+  summarize(p_reads = sum(p_reads), .groups = "drop") %>%
+  group_by(classification, city) %>%
+  summarize(pc_min = min(p_reads)*100, pc_max = max(p_reads)*100, 
+            pc_mean = mean(p_reads)*100, .groups = "drop")
+p_reads_summ_prep <- p_reads_summ_group %>%
+  mutate(classification = fct_inorder(classification),
+         pc_min = pc_min %>% signif(digits=2) %>% sapply(format, scientific=FALSE, trim=TRUE, digits=2),
+         pc_max = pc_max %>% signif(digits=2) %>% sapply(format, scientific=FALSE, trim=TRUE, digits=2),
+         pc_mean = pc_mean %>% signif(digits=2) %>% sapply(format, scientific=FALSE, trim=TRUE, digits=2),
+         display = paste0(pc_min, "-", pc_max, "% (mean ", pc_mean, "%)"))
+p_reads_summ <- p_reads_summ_prep %>%
+  select(city, classification, read_fraction=display) %>%
+  arrange(city, classification)
+p_reads_summ
+```
+
+In many respects, these resemble the Prussin data: high human fraction (mean 19.6%), high bacterial fraction (mean 18.2%), high unclassified fraction (mean 43.9%), low viral fraction (mean 0.01%). One notable difference is that archaeal reads are more abundant (0.034% compared to 0.016% for Prussin).
+
+As in Prussin, viral DNA reads were dominated by *Caudoviricetes* phages. Other viral classes that are prominent in at least some samples include *Herviviricetes* (herpesviruses), *Papovaviricetes* (polyomaviruses and papillomaviruses), *Revtraviricetes* (retroviruses + Hep B), and *Naldaviricetes* (mainly arthropod viruses). I'll investigate the first three of this latter group in more depth, restricting in each case to samples where that family makes up at least 5% of viral reads.
+
+```{r}
+#| label: extract-viral-taxa
+
+# Get viral taxonomy
+viral_taxa_path <- file.path(data_dir_base, "viral-taxids.tsv.gz")
+viral_taxa <- read_tsv(viral_taxa_path, show_col_types = FALSE)
+
+# Get paths to Kraken reports
+samples <- as.character(basic_stats_raw$sample)
+report_dirs <- file.path(data_dirs, "kraken")
+report_paths <- lapply(report_dirs, list.files, full.names = TRUE) %>% unlist
+names(report_paths) <- str_extract(report_paths, "SRR\\d*")
+
+# Extract viral taxa
+col_names <- c("pc_reads_total", "n_reads_clade", "n_reads_direct",
+               "rank", "taxid", "name")
+kraken_reports_raw <- lapply(report_paths, read_tsv, col_names = col_names,
+                             show_col_types = FALSE)
+kraken_reports <- lapply(names(kraken_reports_raw), 
+                         function(x) kraken_reports_raw[[x]] %>% 
+                           mutate(sample = x)) %>% bind_rows
+kraken_reports_viral <- filter(kraken_reports, taxid %in% viral_taxa$taxid) %>%
+  group_by(sample) %>%
+  mutate(p_reads_viral = n_reads_clade/n_reads_clade[1])
+kraken_reports_viral_cleaned <- kraken_reports_viral %>%
+  inner_join(libraries, by="sample") %>%
+  select(-pc_reads_total, -n_reads_direct) %>%
+  select(name, taxid, p_reads_viral, n_reads_clade, everything())
+
+viral_classes <- kraken_reports_viral_cleaned %>% filter(rank == "C")
+viral_families <- kraken_reports_viral_cleaned %>% filter(rank == "F")
+
+```
+
+```{r}
+#| label: viral-class-composition
+
+major_threshold <- 0.05
+
+# Identify major viral classes
+viral_classes_major_tab <- viral_classes %>% 
+  group_by(name, taxid) %>%
+  summarize(p_reads_viral_max = max(p_reads_viral), .groups="drop") %>%
+  filter(p_reads_viral_max >= major_threshold)
+viral_classes_major_list <- viral_classes_major_tab %>% pull(name)
+viral_classes_major <- viral_classes %>% 
+  filter(name %in% viral_classes_major_list) %>%
+  select(name, taxid, sample, date_alias, city, p_reads_viral)
+viral_classes_minor <- viral_classes_major %>% 
+  group_by(sample, date_alias, city) %>%
+  summarize(p_reads_viral_major = sum(p_reads_viral), .groups = "drop") %>%
+  mutate(name = "Other", taxid=NA, p_reads_viral = 1-p_reads_viral_major) %>%
+  select(name, taxid, sample, date_alias, city, p_reads_viral)
+viral_classes_display <- bind_rows(viral_classes_major, viral_classes_minor) %>%
+  arrange(desc(p_reads_viral)) %>% 
+  mutate(name = factor(name, levels=c(viral_classes_major_list, "Other")),
+         p_reads_viral = pmax(p_reads_viral, 0)) %>%
+  rename(p_reads = p_reads_viral, classification=name)
+
+palette_viral <- c(brewer.pal(12, "Set3"), brewer.pal(8, "Dark2"))
+g_classes <- g_comp_base + 
+  geom_col(data=viral_classes_display, position = "stack") +
+  scale_y_continuous(name="% Viral Reads", limits=c(0,1.01), breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral class")
+  
+g_classes
+
+```
+
+*Papovaviricetes* are quite heterogeneous across samples, and frequently diverse within samples. *Alphapolyomavirus* and *Alphapapillomavirus* are the most abundant genera overall, but *Betapapillomavirus, Gammapapillomavirus, Mupapillomavirus* and others all have strong showings.
+
+```{r}
+#| label: papovaviricetes
+#| fig-height: 6
+#| warning: false
+
+# Get samples
+papova_taxid <- 2732421
+papova_threshold <- 0.05
+papova_samples <- viral_classes %>% filter(taxid == papova_taxid) %>% filter(p_reads_viral > 0.05) %>% pull(sample) %>% unique
+
+# Get all taxa in class
+papova_desc_taxids_old <- papova_taxid
+papova_desc_taxids_new <- unique(c(papova_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% papova_desc_taxids_old) %>% pull(taxid)))
+while (length(papova_desc_taxids_new) > length(papova_desc_taxids_old)){
+  papova_desc_taxids_old <- papova_desc_taxids_new
+  papova_desc_taxids_new <- unique(c(papova_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% papova_desc_taxids_old) %>% pull(taxid)))
+}
+
+# Get read counts
+papova_counts <- kraken_reports_viral_cleaned %>%
+  filter(taxid %in% papova_desc_taxids_new,
+         sample %in% papova_samples) %>%
+  mutate(p_reads_papova = n_reads_clade/n_reads_clade[1])
+
+# Get genus composition
+papova_genera <- papova_counts %>% filter(rank == "G")
+papova_genera_major_tab <- papova_genera %>% 
+  group_by(name, taxid) %>%
+  summarize(p_reads_papova_max = max(p_reads_papova), .groups="drop") %>%
+  filter(p_reads_papova_max >= papova_threshold)
+papova_genera_major_list <- papova_genera_major_tab %>% pull(name)
+papova_genera_major <- papova_genera %>% 
+  filter(name %in% papova_genera_major_list) %>%
+  select(name, taxid, sample, date_alias, city, p_reads_papova)
+papova_genera_minor <- papova_genera_major %>% 
+  group_by(sample, date_alias, city) %>%
+  summarize(p_reads_papova_major = sum(p_reads_papova), .groups = "drop") %>%
+  mutate(name = "Other", taxid=NA, p_reads_papova = 1-p_reads_papova_major) %>%
+  select(name, taxid, sample, date_alias, city, p_reads_papova)
+papova_genera_display <- bind_rows(papova_genera_major, papova_genera_minor) %>%
+  arrange(desc(p_reads_papova)) %>% 
+  mutate(name = factor(name, levels=c(papova_genera_major_list, "Other"))) %>%
+  rename(p_reads = p_reads_papova, classification=name)
+
+# Plot
+g_papova_genera <- g_comp_base + 
+  geom_col(data=papova_genera_display, position = "stack") +
+  scale_y_continuous(name="% Papovaviricetes Reads", limits=c(0,1.02), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral genus") +
+  guides(fill=guide_legend(ncol=3))
+g_papova_genera
+
+```
+
+Only a few samples showed at least 5% prevalence of *Herviviricetes*, but those that did were typically dominated by one or a small number of species that varied between samples. Of these, human alphaherpesvirus 1 appeared in the most samples, but several other species were prominent in at least one sample:
+
+```{r}
+#| label: herviviricetes
+#| fig-height: 6
+#| fig-width: 9
+
+# Get samples
+hervi_taxid <- 2731363
+hervi_threshold <- 0.05
+hervi_samples <- viral_classes %>% filter(taxid == hervi_taxid) %>% filter(p_reads_viral > 0.05) %>% pull(sample) %>% unique
+
+# Get all taxa in class
+hervi_desc_taxids_old <- hervi_taxid
+hervi_desc_taxids_new <- unique(c(hervi_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% hervi_desc_taxids_old) %>% pull(taxid)))
+while (length(hervi_desc_taxids_new) > length(hervi_desc_taxids_old)){
+  hervi_desc_taxids_old <- hervi_desc_taxids_new
+  hervi_desc_taxids_new <- unique(c(hervi_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% hervi_desc_taxids_old) %>% pull(taxid)))
+}
+
+# Get read counts
+hervi_counts <- kraken_reports_viral_cleaned %>%
+  filter(taxid %in% hervi_desc_taxids_new,
+         sample %in% hervi_samples) %>%
+  mutate(p_reads_hervi = n_reads_clade/n_reads_clade[1])
+
+# Get genus composition
+hervi_genera <- hervi_counts %>% filter(rank == "S")
+hervi_genera_major_tab <- hervi_genera %>% 
+  group_by(name, taxid) %>%
+  summarize(p_reads_hervi_max = max(p_reads_hervi), .groups="drop") %>%
+  filter(p_reads_hervi_max >= hervi_threshold)
+hervi_genera_major_list <- hervi_genera_major_tab %>% pull(name)
+hervi_genera_major <- hervi_genera %>% 
+  filter(name %in% hervi_genera_major_list) %>%
+  select(name, taxid, sample, date_alias, city, p_reads_hervi)
+hervi_genera_minor <- hervi_genera_major %>% 
+  group_by(sample, date_alias, city) %>%
+  summarize(p_reads_hervi_major = sum(p_reads_hervi), .groups = "drop") %>%
+  mutate(name = "Other", taxid=NA, p_reads_hervi = 1-p_reads_hervi_major) %>%
+  select(name, taxid, sample, date_alias, city, p_reads_hervi)
+hervi_genera_display <- bind_rows(hervi_genera_major, hervi_genera_minor) %>%
+  arrange(desc(p_reads_hervi)) %>% 
+  mutate(name = factor(name, levels=c(hervi_genera_major_list, "Other"))) %>%
+  rename(p_reads = p_reads_hervi, classification=name)
+
+# Plot
+g_hervi_genera <- g_comp_base + 
+  geom_col(data=hervi_genera_display, position = "stack") +
+  scale_y_continuous(name="% Herviviricetes Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral genus") +
+  guides(fill=guide_legend(ncol=3))
+g_hervi_genera
+
+```
+
+Finally, for *Revtraviricetes*, most samples were dominated by porcine type-C oncovirus, while one was dominated by an avian retrovirus. The last showed significant levels of two murine viruses plus HIV. I'm suspicious of many of these.
+
+```{r}
+#| label: revtraviricetes
+#| fig-height: 6
+#| fig-width: 9
+
+# Get samples
+revtra_taxid <- 2732514
+revtra_threshold <- 0.05
+revtra_samples <- viral_classes %>% filter(taxid == revtra_taxid) %>% filter(p_reads_viral > 0.05) %>% pull(sample) %>% unique
+
+# Get all taxa in class
+revtra_desc_taxids_old <- revtra_taxid
+revtra_desc_taxids_new <- unique(c(revtra_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% revtra_desc_taxids_old) %>% pull(taxid)))
+while (length(revtra_desc_taxids_new) > length(revtra_desc_taxids_old)){
+  revtra_desc_taxids_old <- revtra_desc_taxids_new
+  revtra_desc_taxids_new <- unique(c(revtra_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% revtra_desc_taxids_old) %>% pull(taxid)))
+}
+
+# Get read counts
+revtra_counts <- kraken_reports_viral_cleaned %>%
+  filter(taxid %in% revtra_desc_taxids_new,
+         sample %in% revtra_samples) %>%
+  mutate(p_reads_revtra = n_reads_clade/n_reads_clade[1])
+
+# Get genus composition
+revtra_genera <- revtra_counts %>% filter(rank == "S")
+revtra_genera_major_tab <- revtra_genera %>% 
+  group_by(name, taxid) %>%
+  summarize(p_reads_revtra_max = max(p_reads_revtra), .groups="drop") %>%
+  filter(p_reads_revtra_max >= revtra_threshold)
+revtra_genera_major_list <- revtra_genera_major_tab %>% pull(name)
+revtra_genera_major <- revtra_genera %>% 
+  filter(name %in% revtra_genera_major_list) %>%
+  select(name, taxid, sample, date_alias, city, p_reads_revtra)
+revtra_genera_minor <- revtra_genera_major %>% 
+  group_by(sample, date_alias, city) %>%
+  summarize(p_reads_revtra_major = sum(p_reads_revtra), .groups = "drop") %>%
+  mutate(name = "Other", taxid=NA, p_reads_revtra = 1-p_reads_revtra_major) %>%
+  select(name, taxid, sample, date_alias, city, p_reads_revtra)
+revtra_genera_display <- bind_rows(revtra_genera_major, revtra_genera_minor) %>%
+  arrange(desc(p_reads_revtra)) %>% 
+  mutate(name = factor(name, levels=c(revtra_genera_major_list, "Other"))) %>%
+  rename(p_reads = p_reads_revtra, classification=name)
+
+# Plot
+g_revtra_genera <- g_comp_base + 
+  geom_col(data=revtra_genera_display, position = "stack") +
+  scale_y_continuous(name="% revtraviricetes Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral genus") +
+  guides(fill=guide_legend(ncol=3))
+g_revtra_genera
+
+```
+
+# Human-infecting virus reads: validation
+
+Next, I investigated the human-infecting virus read content of these unenriched samples. Using the same workflow I used for Prussin et al, I identified 24,278 read pairs as putatively human viral: 0.002% of reads surviving to that stage in the pipeline.
+
+```{r}
+#| label: hv-read-counts
+
+# Import HV read data
+hv_reads_filtered_paths <- file.path(data_dirs, "hv_hits_putative_filtered.tsv.gz")
+hv_reads_filtered <- lapply(hv_reads_filtered_paths, read_tsv,
+                            show_col_types = FALSE) %>%
+  bind_rows() %>%
+  inner_join(libraries, by="sample")
+
+# Count reads
+n_hv_filtered <- hv_reads_filtered %>%
+  group_by(sample, date, date_alias, city, seq_id) %>% count %>%
+  group_by(sample, date, date_alias, city) %>% count %>% 
+  inner_join(basic_stats %>% filter(stage == "ribo_initial") %>% 
+               select(sample, n_read_pairs), by="sample") %>% 
+  rename(n_putative = n, n_total = n_read_pairs) %>% 
+  mutate(p_reads = n_putative/n_total, pc_reads = p_reads * 100)
+n_hv_filtered_summ <- n_hv_filtered %>% ungroup %>%
+  summarize(n_putative = sum(n_putative), n_total = sum(n_total), 
+            .groups="drop") %>% 
+  mutate(p_reads = n_putative/n_total, pc_reads = p_reads*100)
+```
+
+```{r}
+#| label: plot-hv-scores
+#| warning: false
+#| fig-width: 8
+
+# Collapse multi-entry sequences
+rmax <- purrr::partial(max, na.rm = TRUE)
+collapse <- function(x) ifelse(all(x == x[1]), x[1], paste(x, collapse="/"))
+mrg <- hv_reads_filtered %>% 
+  mutate(adj_score_max = pmax(adj_score_fwd, adj_score_rev, na.rm = TRUE)) %>%
+  arrange(desc(adj_score_max)) %>%
+  group_by(seq_id) %>%
+  summarize(sample = collapse(sample),
+            genome_id = collapse(genome_id),
+            taxid_best = taxid[1],
+            taxid = collapse(as.character(taxid)),
+            best_alignment_score_fwd = rmax(best_alignment_score_fwd),
+            best_alignment_score_rev = rmax(best_alignment_score_rev),
+            query_len_fwd = rmax(query_len_fwd),
+            query_len_rev = rmax(query_len_rev),
+            query_seq_fwd = query_seq_fwd[!is.na(query_seq_fwd)][1],
+            query_seq_rev = query_seq_rev[!is.na(query_seq_rev)][1],
+            classified = rmax(classified),
+            assigned_name = collapse(assigned_name),
+            assigned_taxid_best = assigned_taxid[1],
+            assigned_taxid = collapse(as.character(assigned_taxid)),
+            assigned_hv = rmax(assigned_hv),
+            hit_hv = rmax(hit_hv),
+            encoded_hits = collapse(encoded_hits),
+            adj_score_fwd = rmax(adj_score_fwd),
+            adj_score_rev = rmax(adj_score_rev)
+            ) %>%
+  inner_join(libraries, by="sample") %>%
+  mutate(kraken_label = ifelse(assigned_hv, "Kraken2 HV\nassignment",
+                               ifelse(hit_hv, "Kraken2 HV\nhit",
+                                      "No hit or\nassignment"))) %>%
+  mutate(adj_score_max = pmax(adj_score_fwd, adj_score_rev),
+         highscore = adj_score_max >= 20)
+
+g_hist_0 <- ggplot(mrg, aes(x=adj_score_max)) + 
+  geom_histogram(binwidth=5,boundary=0) +
+  geom_vline(xintercept=20, linetype="dashed", color="red") +
+  facet_wrap(~kraken_label, labeller = labeller(kit = label_wrap_gen(20)), scales = "free_y") +
+  scale_x_continuous(name = "Maximum adjusted alignment score") + 
+  scale_y_continuous(name="# Read pairs") + 
+  theme_base 
+g_hist_0
+```
+
+As previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I've used for previous datasets.
+
+```{r}
+#| label: make-blast-fasta
+mrg_fasta <-  mrg %>%
+  mutate(seq_head = paste0(">", seq_id)) %>%
+  ungroup %>%
+  select(header1=seq_head, seq1=query_seq_fwd, 
+         header2=seq_head, seq2=query_seq_rev) %>%
+  mutate(header1=paste0(header1, "_1"), header2=paste0(header2, "_2"))
+mrg_fasta_sep <- bind_rows(select(mrg_fasta, header=header1, seq=seq1),
+                           select(mrg_fasta, header=header2, seq=seq2)) %>%
+  filter(!is.na(seq))
+mrg_fasta_out <- do.call(paste, c(mrg_fasta_sep, sep="\n")) %>% 
+  paste(collapse="\n")
+blast_dir <- file.path(data_dir_base, "blast")
+dir.create(blast_dir, showWarnings = FALSE)
+write(mrg_fasta_out, file.path(blast_dir, "putative-viral.fasta"))
+```
+
+```{r}
+#| label: process-blast-data
+#| warning: false
+
+# Import BLAST results
+# blast_results_path <- file.path(data_dir_base, "blast/putative-viral.blast.gz")
+# blast_cols <- c("qseqid", "sseqid", "sgi", "staxid", "qlen", "evalue", "bitscore", "qcovs", "length", "pident", "mismatch", "gapopen", "sstrand", "qstart", "qend", "sstart", "send")
+# blast_results <- read_tsv(blast_results_path, show_col_types = FALSE,
+#                           col_names = blast_cols, col_types = cols(.default="c"))
+blast_results_path <- file.path(data_dir_base, "blast/putative-viral-best.blast.gz")
+blast_results <- read_tsv(blast_results_path, show_col_types = FALSE)
+
+# Filter for best hit for each query/subject combination
+blast_results_best <- blast_results %>% group_by(qseqid, staxid) %>% 
+  filter(bitscore == max(bitscore)) %>%
+  filter(length == max(length)) %>% filter(row_number() == 1)
+write_tsv(blast_results_best, file.path(data_dir_base, "blast/putative-viral-best.blast.gz"))
+
+# Rank hits for each query and filter for high-ranking hits
+blast_results_ranked <- blast_results_best %>% 
+  group_by(qseqid) %>% mutate(rank = dense_rank(desc(bitscore)))
+blast_results_highrank <- blast_results_ranked %>% filter(rank <= 5) %>%
+    mutate(read_pair = str_split(qseqid, "_") %>% sapply(nth, n=-1), 
+         seq_id = str_split(qseqid, "_") %>% sapply(nth, n=1)) %>%
+    mutate(bitscore = as.numeric(bitscore))
+
+# Summarize by read pair and taxid
+blast_results_paired <- blast_results_highrank %>%
+  group_by(seq_id, staxid) %>%
+  summarize(bitscore_max = max(bitscore), bitscore_min = min(bitscore),
+            n_reads = n(), .groups = "drop")
+
+# Add viral status
+blast_results_viral <- mutate(blast_results_paired, viral = staxid %in% viral_taxa$taxid) %>%
+  mutate(viral_full = viral & n_reads == 2)
+
+# Compare to Kraken & Bowtie assignments
+match_taxid <- function(taxid_1, taxid_2){
+  p1 <- mapply(grepl, paste0("/", taxid_1, "$"), taxid_2)
+  p2 <- mapply(grepl, paste0("^", taxid_1, "/"), taxid_2)
+  p3 <- mapply(grepl, paste0("^", taxid_1, "$"), taxid_2)
+  out <- setNames(p1|p2|p3, NULL)
+  return(out)
+}
+mrg_assign <- mrg %>% select(sample, seq_id, taxid, assigned_taxid, adj_score_max)
+blast_results_assign <- inner_join(blast_results_viral, mrg_assign, by="seq_id") %>%
+    mutate(taxid_match_bowtie = match_taxid(staxid, taxid),
+           taxid_match_kraken = match_taxid(staxid, assigned_taxid),
+           taxid_match_any = taxid_match_bowtie | taxid_match_kraken)
+blast_results_out <- blast_results_assign %>%
+  group_by(seq_id) %>%
+  summarize(viral_status = ifelse(any(viral_full), 2,
+                                  ifelse(any(taxid_match_any), 2,
+                                             ifelse(any(viral), 1, 0))),
+            .groups = "drop")
+```
+
+```{r}
+#| label: plot-blast-results
+#| fig-height: 6
+#| warning: false
+
+# Merge BLAST results with unenriched read data
+mrg_blast <- full_join(mrg, blast_results_out, by="seq_id") %>%
+  mutate(viral_status = replace_na(viral_status, 0),
+         viral_status_out = ifelse(viral_status == 0, FALSE, TRUE))
+
+# Plot RNA
+g_hist_1 <- ggplot(mrg_blast, aes(x=adj_score_max, fill=viral_status_out)) + 
+  geom_histogram(binwidth=5,boundary=0) + 
+  geom_vline(xintercept=20, linetype="dashed", color="red") +
+  facet_wrap(~kraken_label, labeller = labeller(kit = label_wrap_gen(20)), scales = "free_y") +
+  scale_x_continuous(name = "Maximum adjusted alignment score") + 
+  scale_y_continuous(name="# Read pairs") + 
+  scale_fill_brewer(palette = "Set1", name = "Viral status") +
+  theme_base
+g_hist_1
+```
+
+For a disjunctive score threshold of 20, the workflow achieves a measured F1 score of 98.0%.
+
+```{r}
+#| label: plot-f1
+test_sens_spec <- function(tab, score_threshold){
+  tab_retained <- tab %>% 
+    mutate(retain_score = (adj_score_fwd > score_threshold | adj_score_rev > score_threshold),
+           retain = assigned_hv | hit_hv | retain_score) %>%
+    group_by(viral_status_out, retain) %>% count
+  pos_tru <- tab_retained %>% filter(viral_status_out == "TRUE", retain) %>% pull(n) %>% sum
+  pos_fls <- tab_retained %>% filter(viral_status_out != "TRUE", retain) %>% pull(n) %>% sum
+  neg_tru <- tab_retained %>% filter(viral_status_out != "TRUE", !retain) %>% pull(n) %>% sum
+  neg_fls <- tab_retained %>% filter(viral_status_out == "TRUE", !retain) %>% pull(n) %>% sum
+  sensitivity <- pos_tru / (pos_tru + neg_fls)
+  specificity <- neg_tru / (neg_tru + pos_fls)
+  precision   <- pos_tru / (pos_tru + pos_fls)
+  f1 <- 2 * precision * sensitivity / (precision + sensitivity)
+  out <- tibble(threshold=score_threshold, sensitivity=sensitivity, 
+                specificity=specificity, precision=precision, f1=f1)
+  return(out)
+}
+range_f1 <- function(intab, inrange=15:45){
+  tss <- purrr::partial(test_sens_spec, tab=intab)
+  stats <- lapply(inrange, tss) %>% bind_rows %>%
+    pivot_longer(!threshold, names_to="metric", values_to="value")
+  return(stats)
+}
+stats_0 <- range_f1(mrg_blast)
+g_stats_0 <- ggplot(stats_0, aes(x=threshold, y=value, color=metric)) +
+  geom_vline(xintercept=20, color = "red", linetype = "dashed") +
+  geom_line() +
+  scale_y_continuous(name = "Value", limits=c(0,1), breaks = seq(0,1,0.2), expand = c(0,0)) +
+  scale_x_continuous(name = "Adjusted Score Threshold", expand = c(0,0)) +
+  scale_color_brewer(palette="Dark2") +
+  theme_base
+g_stats_0
+stats_0 %>% filter(threshold == 20) %>% 
+  select(Threshold=threshold, Metric=metric, Value=value)
+```
+
+Looking into the composition of different read groups, the notable observation for me is the high prevalence of Pigeon torque teno virus among high-scoring false positives, with 77 such read pairs. BLAST maps these not to viruses but to their most common hosts, i.e. assorted species of pigeon. That said, the number of false positive PTTV reads is substantially exceeded by the number of true-positive PTTV reads (1883), which do map to appropriate viruses according to BLAST, so the presence of a comparatively small number of false positives seems unlikely to cause too much distortion.
+
+```{r}
+#| label: fp
+#| fig-height: 5
+
+major_threshold <- 0.04
+
+# Add missing viral taxa
+viral_taxa$name[viral_taxa$taxid == 211787] <- "Human papillomavirus type 92"
+viral_taxa$name[viral_taxa$taxid == 509154] <- "Porcine endogenous retrovirus C"
+viral_taxa$name[viral_taxa$taxid == 493803] <- "Merkel cell polyomavirus"
+viral_taxa$name[viral_taxa$taxid == 427343] <- "Human papillomavirus 107"
+viral_taxa$name[viral_taxa$taxid == 194958] <- "Porcine endogenous retrovirus A"
+viral_taxa$name[viral_taxa$taxid == 340907] <- "Papiine alphaherpesvirus 2"
+viral_taxa$name[viral_taxa$taxid == 194959] <- "Porcine endogenous retrovirus B"
+
+
+# Prepare data
+fp <- mrg_blast %>% 
+  group_by(viral_status_out, highscore, taxid_best) %>% count %>% 
+  group_by(viral_status_out, highscore) %>% mutate(p=n/sum(n)) %>% 
+  rename(taxid = taxid_best) %>%
+  left_join(viral_taxa, by="taxid") %>%
+  arrange(desc(p))
+fp_major_tab <- fp %>% filter(p > major_threshold) %>% arrange(desc(p))
+fp_major_list <- fp_major_tab %>% pull(name) %>% sort %>% unique %>% c(., "Other")
+fp_major <- fp %>% mutate(major = p > major_threshold) %>% 
+  mutate(name_display = ifelse(major, name, "Other")) %>%
+  group_by(viral_status_out, highscore, name_display) %>% 
+  summarize(n=sum(n), p=sum(p), .groups = "drop")  %>%
+  mutate(name_display = factor(name_display, levels = fp_major_list),
+         score_display = ifelse(highscore, "S >= 20", "S < 20"),
+         status_display = ifelse(viral_status_out, "True positive", "False positive"))
+
+# Plot
+g_fp <- ggplot(fp_major, aes(x=score_display, y=p, fill=name_display)) +
+  geom_col(position="stack") +
+  scale_x_discrete(name = "True positive?") +
+  scale_y_continuous(name = "% reads", limits = c(0,1.01), 
+                     breaks = seq(0,1,0.2), expand = c(0,0)) +
+  scale_fill_manual(values = palette_viral, name = "Viral\ntaxon") +
+  facet_grid(.~status_display) +
+  guides(fill=guide_legend(ncol=3)) +
+  theme_kit
+g_fp
+
+```
+
+```{r}
+#| label: ptt-blast-hits
+
+# Configure
+ref_taxid_ptt <- 2233536
+p_threshold <- 0.3
+
+# Get taxon names
+tax_names_path <- file.path(data_dir_base, "taxid-names.tsv.gz")
+tax_names <- read_tsv(tax_names_path, show_col_types = FALSE)
+
+# Add missing names
+tax_names_new <- tribble(~staxid, ~name,
+                         3050295, "Cytomegalovirus humanbeta5",
+                         459231, "FLAG-tagging vector pFLAG97-TSR",
+                         3082113, "Rangifer tarandus platyrhynchus",
+                         3119969, "Bubalus kerabau",
+                         177155, "Streptopelia turtur",
+                         187126, "Nesoenas mayeri"
+                         )
+tax_names <- tax_names_new %>% filter(! staxid %in% tax_names$staxid) %>%
+  bind_rows(tax_names) %>% arrange(staxid)
+ref_name_ptt <- tax_names %>% filter(staxid == ref_taxid_ptt) %>% pull(name)
+
+# Get major matches
+mrg_staxid <- mrg_blast %>% filter(taxid_best == ref_taxid_ptt) %>%
+    group_by(highscore, viral_status_out) %>% mutate(n_seq = n())
+fp_staxid <- mrg_staxid %>%
+  left_join(blast_results_paired, by="seq_id") %>%
+  mutate(staxid = as.integer(staxid)) %>%
+  left_join(tax_names, by="staxid") %>% rename(sname=name) %>%
+  left_join(tax_names %>% rename(taxid_best=staxid), by="taxid_best")
+fp_staxid_count <- fp_staxid %>%
+  group_by(viral_status_out, highscore, 
+           taxid_best, name, staxid, sname, n_seq) %>%
+  count %>%
+  group_by(viral_status_out, highscore, taxid_best, name) %>%
+  mutate(p=n/n_seq)
+fp_staxid_count_major <- fp_staxid_count %>%
+  filter(n>1, p>p_threshold, !is.na(staxid)) %>%
+  mutate(score_display = ifelse(highscore, "S >= 20", "S < 20"),
+         status_display = ifelse(viral_status_out, 
+                                 "True positive", "False positive"))
+
+# Plot
+g <- ggplot(fp_staxid_count_major, aes(x=p, y=sname)) + 
+  geom_col() + 
+  facet_grid(status_display~score_display, scales="free",
+             labeller = label_wrap_gen(multi_line = FALSE)) +
+  scale_x_continuous(name="% mapped reads", limits=c(0,1), breaks=seq(0,1,0.2),
+                     expand=c(0,0)) +
+  labs(title=paste0(ref_name_ptt, " (taxid ", ref_taxid_ptt, ")")) +
+  theme_base + theme(
+    axis.title.y = element_blank(),
+    plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))
+g
+```
+
+# Human-infecting viruses: overall relative abundance
+
+```{r}
+#| label: count-hv-reads
+
+# Get raw read counts
+read_counts_raw <- basic_stats_raw %>%
+  select(sample, date_alias, city, n_reads_raw = n_read_pairs)
+
+# Get HV read counts
+mrg_hv <- mrg %>% mutate(hv_status = assigned_hv | hit_hv | highscore) %>%
+  rename(taxid_all = taxid, taxid = taxid_best)
+read_counts_hv <- mrg_hv %>% filter(hv_status) %>% group_by(sample) %>% 
+  count(name="n_reads_hv")
+read_counts <- read_counts_raw %>% left_join(read_counts_hv, by="sample") %>%
+  mutate(n_reads_hv = replace_na(n_reads_hv, 0))
+
+# Aggregate
+read_counts_city <- read_counts %>% group_by(city) %>%
+  summarize(n_reads_raw = sum(n_reads_raw),
+            n_reads_hv = sum(n_reads_hv), .groups="drop") %>%
+  mutate(sample= "All samples", date_alias = "All dates")
+read_counts_total <- read_counts_city %>% group_by(sample, date_alias) %>%
+  summarize(n_reads_raw = sum(n_reads_raw),
+            n_reads_hv = sum(n_reads_hv), .groups="drop") %>%
+  mutate(city = "All cities")
+read_counts_agg <- read_counts_city %>% arrange(city) %>%
+  bind_rows(read_counts_total) %>%
+  mutate(p_reads_hv = n_reads_hv/n_reads_raw,
+         city = fct_inorder(city))
+```
+
+Applying a disjunctive cutoff at S=20 identifies 23,191 read pairs as human-viral. This gives an overall relative HV abundance of $1.73 \times 10^{-5}$.
+
+```{r}
+#| label: plot-hv-ra
+#| warning: false
+# Visualize
+g_phv_agg <- ggplot(read_counts_agg, aes(x=city, color=city)) +
+  geom_point(aes(y=p_reads_hv)) +
+  scale_y_log10("Relative abundance of human virus reads") +
+  scale_x_discrete(name="Collection Date") +
+  #facet_grid(.~sample_type, scales = "free", space = "free_x") +
+  scale_color_city() + theme_rotate
+g_phv_agg
+```
+
+This is lower than for DNA reads from other air-sampling datasets I've analyzed, but not drastically so:
+
+```{r}
+#| label: ra-hv-past
+
+# Collate past RA values
+ra_past <- tribble(~dataset, ~ra, ~na_type, ~panel_enriched,
+                   "Brumfield", 5e-5, "RNA", FALSE,
+                   "Brumfield", 3.66e-7, "DNA", FALSE,
+                   "Spurbeck", 5.44e-6, "RNA", FALSE,
+                   "Yang", 3.62e-4, "RNA", FALSE,
+                   "Rothman (unenriched)", 1.87e-5, "RNA", FALSE,
+                   "Rothman (panel-enriched)", 3.3e-5, "RNA", TRUE,
+                   "Crits-Christoph (unenriched)", 1.37e-5, "RNA", FALSE,
+                   "Crits-Christoph (panel-enriched)", 1.26e-2, "RNA", TRUE,
+                   "Prussin (non-control)", 1.63e-5, "RNA", FALSE,
+                   "Prussin (non-control)", 4.16e-5, "DNA", FALSE,
+                   "Rosario (non-control)", 1.21e-5, "RNA", FALSE,
+                   "Rosario (non-control)", 1.50e-4, "DNA", FALSE
+)
+
+# Collate new RA values
+ra_new <- tribble(~dataset, ~ra, ~na_type, ~panel_enriched,
+                  "Leung", 1.73e-5, "DNA", FALSE)
+
+
+# Plot
+scale_color_na <- purrr::partial(scale_color_brewer, palette="Set1",
+                                 name="Nucleic acid type")
+ra_comp <- bind_rows(ra_past, ra_new) %>% mutate(dataset = fct_inorder(dataset))
+g_ra_comp <- ggplot(ra_comp, aes(y=dataset, x=ra, color=na_type)) +
+  geom_point() +
+  scale_color_na() +
+  scale_x_log10(name="Relative abundance of human virus reads") +
+  theme_base + theme(axis.title.y = element_blank())
+g_ra_comp
+```
+
+# Human-infecting viruses: taxonomy and composition
+
+At the family level, most samples across all cities are dominated by *Papillomaviridae*, *Herpesviridae*, *Anelloviridae*, *Polyomaviridae, and to a lesser extent* *Poxviridae*:
+
+```{r}
+#| label: raise-hv-taxa
+
+# Get viral taxon names for putative HV reads
+viral_taxa$name[viral_taxa$taxid == 249588] <- "Mamastrovirus"
+viral_taxa$name[viral_taxa$taxid == 194960] <- "Kobuvirus"
+viral_taxa$name[viral_taxa$taxid == 688449] <- "Salivirus"
+viral_taxa$name[viral_taxa$taxid == 585893] <- "Picobirnaviridae"
+viral_taxa$name[viral_taxa$taxid == 333922] <- "Betapapillomavirus"
+viral_taxa$name[viral_taxa$taxid == 334207] <- "Betapapillomavirus 3"
+viral_taxa$name[viral_taxa$taxid == 369960] <- "Porcine type-C oncovirus"
+viral_taxa$name[viral_taxa$taxid == 333924] <- "Betapapillomavirus 2"
+viral_taxa$name[viral_taxa$taxid == 687329] <- "Anelloviridae"
+viral_taxa$name[viral_taxa$taxid == 325455] <- "Gammapapillomavirus"
+viral_taxa$name[viral_taxa$taxid == 333750] <- "Alphapapillomavirus"
+viral_taxa$name[viral_taxa$taxid == 694002] <- "Betacoronavirus"
+viral_taxa$name[viral_taxa$taxid == 334202] <- "Mupapillomavirus"
+viral_taxa$name[viral_taxa$taxid == 197911] <- "Alphainfluenzavirus"
+viral_taxa$name[viral_taxa$taxid == 186938] <- "Respirovirus"
+viral_taxa$name[viral_taxa$taxid == 333926] <- "Gammapapillomavirus 1"
+viral_taxa$name[viral_taxa$taxid == 337051] <- "Betapapillomavirus 1"
+viral_taxa$name[viral_taxa$taxid == 337043] <- "Alphapapillomavirus 4"
+viral_taxa$name[viral_taxa$taxid == 694003] <- "Betacoronavirus 1"
+viral_taxa$name[viral_taxa$taxid == 334204] <- "Mupapillomavirus 2"
+viral_taxa$name[viral_taxa$taxid == 334208] <- "Betapapillomavirus 4"
+viral_taxa$name[viral_taxa$taxid == 333928] <- "Gammapapillomavirus 2"
+viral_taxa$name[viral_taxa$taxid == 337039] <- "Alphapapillomavirus 2"
+viral_taxa$name[viral_taxa$taxid == 333929] <- "Gammapapillomavirus 3"
+viral_taxa$name[viral_taxa$taxid == 337042] <- "Alphapapillomavirus 7"
+viral_taxa$name[viral_taxa$taxid == 334203] <- "Mupapillomavirus 1"
+viral_taxa$name[viral_taxa$taxid == 333757] <- "Alphapapillomavirus 8"
+viral_taxa$name[viral_taxa$taxid == 337050] <- "Alphapapillomavirus 6"
+viral_taxa$name[viral_taxa$taxid == 333767] <- "Alphapapillomavirus 3"
+viral_taxa$name[viral_taxa$taxid == 333754] <- "Alphapapillomavirus 10"
+viral_taxa$name[viral_taxa$taxid == 687363] <- "Torque teno virus 24"
+viral_taxa$name[viral_taxa$taxid == 687342] <- "Torque teno virus 3"
+viral_taxa$name[viral_taxa$taxid == 687359] <- "Torque teno virus 20"
+viral_taxa$name[viral_taxa$taxid == 194441] <- "Primate T-lymphotropic virus 2"
+viral_taxa$name[viral_taxa$taxid == 334209] <- "Betapapillomavirus 5"
+
+
+mrg_hv_named <- mrg_hv %>% left_join(viral_taxa, by="taxid")
+
+# Discover viral species & genera for HV reads
+raise_rank <- function(read_db, taxid_db, out_rank = "species", verbose = FALSE){
+  # Get higher ranks than search rank
+  ranks <- c("subspecies", "species", "subgenus", "genus", "subfamily", "family", "suborder", "order", "class", "subphylum", "phylum", "kingdom", "superkingdom")
+  rank_match <- which.max(ranks == out_rank)
+  high_ranks <- ranks[rank_match:length(ranks)]
+  # Merge read DB and taxid DB
+  reads <- read_db %>% select(-parent_taxid, -rank, -name) %>%
+    left_join(taxid_db, by="taxid")
+  # Extract sequences that are already at appropriate rank
+  reads_rank <- filter(reads, rank == out_rank)
+  # Drop sequences at a higher rank and return unclassified sequences
+  reads_norank <- reads %>% filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))
+  while(nrow(reads_norank) > 0){ # As long as there are unclassified sequences...
+    # Promote read taxids and re-merge with taxid DB, then re-classify and filter
+    reads_remaining <- reads_norank %>% mutate(taxid = parent_taxid) %>%
+      select(-parent_taxid, -rank, -name) %>%
+      left_join(taxid_db, by="taxid")
+    reads_rank <- reads_remaining %>% filter(rank == out_rank) %>%
+      bind_rows(reads_rank)
+    reads_norank <- reads_remaining %>%
+      filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))
+  }
+  # Finally, extract and append reads that were excluded during the process
+  reads_dropped <- reads %>% filter(!seq_id %in% reads_rank$seq_id)
+  reads_out <- reads_rank %>% bind_rows(reads_dropped) %>%
+    select(-parent_taxid, -rank, -name) %>%
+    left_join(taxid_db, by="taxid")
+  return(reads_out)
+}
+hv_reads_species <- raise_rank(mrg_hv_named, viral_taxa, "species")
+hv_reads_genus <- raise_rank(mrg_hv_named, viral_taxa, "genus")
+hv_reads_family <- raise_rank(mrg_hv_named, viral_taxa, "family")
+```
+
+```{r}
+#| label: hv-family
+#| fig-height: 6
+#| fig-width: 9
+
+threshold_major_family <- 0.05
+
+# Count reads for each human-viral family
+hv_family_counts <- hv_reads_family %>% 
+  group_by(sample, date_alias, city, name, taxid) %>%
+  count(name = "n_reads_hv") %>%
+  group_by(sample, date_alias, city) %>%
+  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))
+
+# Identify high-ranking families and group others
+hv_family_major_tab <- hv_family_counts %>% group_by(name) %>% 
+  filter(p_reads_hv == max(p_reads_hv)) %>% filter(row_number() == 1) %>%
+  arrange(desc(p_reads_hv)) %>% filter(p_reads_hv > threshold_major_family)
+hv_family_counts_major <- hv_family_counts %>%
+  mutate(name_display = ifelse(name %in% hv_family_major_tab$name, name, "Other")) %>%
+  group_by(sample, date_alias, city, name_display) %>%
+  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), 
+            .groups="drop") %>%
+  mutate(name_display = factor(name_display, 
+                               levels = c(hv_family_major_tab$name, "Other")))
+hv_family_counts_display <- hv_family_counts_major %>%
+  rename(p_reads = p_reads_hv, classification = name_display)
+
+# Plot
+g_hv_family <- g_comp_base + 
+  geom_col(data=hv_family_counts_display, position = "stack") +
+  scale_y_continuous(name="% HV Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral family") +
+  labs(title="Family composition of human-viral reads") +
+  guides(fill=guide_legend(ncol=4)) +
+  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))
+g_hv_family
+```
+
+In investigating individual viral families, to avoid distortions from a few rare reads, I restricted myself to samples where that family made up at least 10% of human-viral reads.
+
+As usual, *Papillomaviridae* reads are divided among many different viral species. In this case, Betapapillomavirus 1 and 2 are the most prevalent across samples, but many other alpha-, beta-, gamma-, and mupapillomaviruses are highly prevalent in at least some samples.
+
+```{r}
+#| label: hv-species-papilloma
+#| fig-height: 6
+#| fig-width: 9
+
+
+threshold_major_species <- 0.4
+taxid_papilloma <- 151340
+
+# Get set of Papillomaviridae reads
+papilloma_samples <- hv_family_counts %>% filter(taxid == taxid_papilloma) %>%
+  filter(p_reads_hv >= 0.1) %>%
+  pull(sample)
+papilloma_ids <- hv_reads_family %>% 
+  filter(taxid == taxid_papilloma, sample %in% papilloma_samples) %>%
+  pull(seq_id)
+
+# Count reads for each Papillomaviridae species
+papilloma_species_counts <- hv_reads_species %>%
+  filter(seq_id %in% papilloma_ids) %>%
+  group_by(sample, date_alias, city, name, taxid) %>%
+  count(name = "n_reads_hv") %>%
+  group_by(sample, date_alias, city) %>%
+  mutate(p_reads_papilloma = n_reads_hv/sum(n_reads_hv))
+
+# Identify high-ranking families and group others
+papilloma_species_major_tab <- papilloma_species_counts %>% group_by(name) %>% 
+  filter(p_reads_papilloma == max(p_reads_papilloma)) %>% 
+  filter(row_number() == 1) %>%
+  arrange(desc(p_reads_papilloma)) %>% 
+  filter(p_reads_papilloma > threshold_major_species)
+papilloma_species_counts_major <- papilloma_species_counts %>%
+  mutate(name_display = ifelse(name %in% papilloma_species_major_tab$name, 
+                               name, "Other")) %>%
+  group_by(sample, date_alias, city, name_display) %>%
+  summarize(n_reads_papilloma = sum(n_reads_hv),
+            p_reads_papilloma = sum(p_reads_papilloma), 
+            .groups="drop") %>%
+  mutate(name_display = factor(name_display, 
+                               levels = c(papilloma_species_major_tab$name, "Other")))
+papilloma_species_counts_display <- papilloma_species_counts_major %>%
+  rename(p_reads = p_reads_papilloma, classification = name_display)
+
+# Plot
+g_papilloma_species <- g_comp_base + 
+  geom_col(data=papilloma_species_counts_display, position = "stack") +
+  scale_y_continuous(name="% Papillomaviridae Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral species") +
+  labs(title="Species composition of Papillomaviridae reads") +
+  guides(fill=guide_legend(ncol=3)) +
+  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))
+
+g_papilloma_species
+
+# Get most prominent species for text
+papilloma_species_collate <- papilloma_species_counts %>% group_by(name, taxid) %>% 
+  summarize(n_reads_tot = sum(n_reads_hv), p_reads_mean = mean(p_reads_papilloma), .groups="drop") %>% 
+  arrange(desc(n_reads_tot))
+```
+
+In terms of total reads across samples, herpesviruses are dominated by Epstein-Barr virus (Human gammaherpesvirus 4), HSV-1 (Human alphaherpesvirus 1), and human cytomegalovirus (Human betaherpesvirus 5). However, numerous other herpesviruses are also present.
+
+```{r}
+#| label: hv-species-herpes
+#| fig-height: 6
+#| fig-width: 9
+
+threshold_major_species <- 0.4
+taxid_herpes <- viral_taxa %>% filter(name == "Herpesviridae") %>% pull(taxid)
+
+# Get set of herpesviridae reads
+herpes_samples <- hv_family_counts %>% filter(taxid == taxid_herpes) %>%
+  filter(p_reads_hv >= 0.1) %>%
+  pull(sample)
+herpes_ids <- hv_reads_family %>% 
+  filter(taxid == taxid_herpes, sample %in% herpes_samples) %>%
+  pull(seq_id)
+
+# Count reads for each herpesviridae species
+herpes_species_counts <- hv_reads_species %>%
+  filter(seq_id %in% herpes_ids) %>%
+  group_by(sample, date_alias, city, name, taxid) %>%
+  count(name = "n_reads_hv") %>%
+  group_by(sample, date_alias, city) %>%
+  mutate(p_reads_herpes = n_reads_hv/sum(n_reads_hv))
+
+# Identify high-ranking families and group others
+herpes_species_major_tab <- herpes_species_counts %>% group_by(name) %>% 
+  filter(p_reads_herpes == max(p_reads_herpes)) %>% 
+  filter(row_number() == 1) %>%
+  arrange(desc(p_reads_herpes)) %>% 
+  filter(p_reads_herpes > threshold_major_species)
+herpes_species_counts_major <- herpes_species_counts %>%
+  mutate(name_display = ifelse(name %in% herpes_species_major_tab$name, 
+                               name, "Other")) %>%
+  group_by(sample, date_alias, city, name_display) %>%
+  summarize(n_reads_herpes = sum(n_reads_hv),
+            p_reads_herpes = sum(p_reads_herpes), 
+            .groups="drop") %>%
+  mutate(name_display = factor(name_display, 
+                               levels = c(herpes_species_major_tab$name, "Other")))
+herpes_species_counts_display <- herpes_species_counts_major %>%
+  rename(p_reads = p_reads_herpes, classification = name_display)
+
+# Plot
+g_herpes_species <- g_comp_base + 
+  geom_col(data=herpes_species_counts_display, position = "stack") +
+  scale_y_continuous(name="% herpesviridae Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral species") +
+  labs(title="Species composition of Herpesviridae reads") +
+  guides(fill=guide_legend(ncol=3)) +
+  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))
+
+g_herpes_species
+
+# Get most prominent species for text
+herpes_species_collate <- herpes_species_counts %>% group_by(name, taxid) %>% 
+  summarize(n_reads_tot = sum(n_reads_hv), p_reads_mean = mean(p_reads_herpes), .groups="drop") %>% 
+  arrange(desc(n_reads_tot))
+```
+
+In sharp contrast to the above, my pipeline classifies the great majority of anellovirus reads in all samples into a single species, torque teno virus. Looking online, it looks like there are a lot of "torque teno viruses" within *Anelloviridae* – for example, Wikipedia says that the genus *Alphatorquevirus* contains \>20 numbered torque teno viruses – so I'm not sure exactly which virus this refers to.
+
+```{r}
+#| label: hv-species-anello
+#| fig-height: 6
+
+threshold_major_species <- 0.1
+taxid_anello <- viral_taxa %>% filter(name == "Anelloviridae") %>% pull(taxid)
+
+# Get set of anelloviridae reads
+anello_samples <- hv_family_counts %>% filter(taxid == taxid_anello) %>%
+  filter(p_reads_hv >= 0.1) %>%
+  pull(sample)
+anello_ids <- hv_reads_family %>% 
+  filter(taxid == taxid_anello, sample %in% anello_samples) %>%
+  pull(seq_id)
+
+# Count reads for each anelloviridae species
+anello_species_counts <- hv_reads_species %>%
+  filter(seq_id %in% anello_ids) %>%
+  group_by(sample, date_alias, city, name, taxid) %>%
+  count(name = "n_reads_hv") %>%
+  group_by(sample, date_alias, city) %>%
+  mutate(p_reads_anello = n_reads_hv/sum(n_reads_hv))
+
+# Identify high-ranking families and group others
+anello_species_major_tab <- anello_species_counts %>% group_by(name) %>% 
+  filter(p_reads_anello == max(p_reads_anello)) %>% 
+  filter(row_number() == 1) %>%
+  arrange(desc(p_reads_anello)) %>% 
+  filter(p_reads_anello > threshold_major_species)
+anello_species_counts_major <- anello_species_counts %>%
+  mutate(name_display = ifelse(name %in% anello_species_major_tab$name, 
+                               name, "Other")) %>%
+  group_by(sample, date_alias, city, name_display) %>%
+  summarize(n_reads_anello = sum(n_reads_hv),
+            p_reads_anello = sum(p_reads_anello), 
+            .groups="drop") %>%
+  mutate(name_display = factor(name_display, 
+                               levels = c(anello_species_major_tab$name, "Other")))
+anello_species_counts_display <- anello_species_counts_major %>%
+  rename(p_reads = p_reads_anello, classification = name_display)
+
+# Plot
+g_anello_species <- g_comp_base + 
+  geom_col(data=anello_species_counts_display, position = "stack") +
+  scale_y_continuous(name="% Anelloviridae Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral species") +
+  labs(title="Species composition of Anelloviridae reads") +
+  guides(fill=guide_legend(ncol=4)) +
+  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))
+
+g_anello_species
+
+# Get most prominent species for text
+anello_species_collate <- anello_species_counts %>% group_by(name, taxid) %>% 
+  summarize(n_reads_tot = sum(n_reads_hv), p_reads_mean = mean(p_reads_anello), .groups="drop") %>% 
+  arrange(desc(n_reads_tot))
+```
+
+Polyomaviruses are intermediate; most viruses are dominated by a single species, *Alphapolyomavirus quintihominis*, but several other viruses in the family are also present.
+
+```{r}
+#| label: hv-species-polyoma
+#| fig-height: 6
+#| fig-width: 9
+
+threshold_major_species <- 0.1
+taxid_polyoma <- viral_taxa %>% filter(name == "Polyomaviridae") %>% pull(taxid)
+
+# Get set of polyomaviridae reads
+# Get set of polyomaviridae reads
+polyoma_samples <- hv_family_counts %>% filter(taxid == taxid_polyoma) %>%
+  filter(p_reads_hv >= 0.1) %>%
+  pull(sample)
+polyoma_ids <- hv_reads_family %>% 
+  filter(taxid == taxid_polyoma, sample %in% polyoma_samples) %>%
+  pull(seq_id)
+
+# Count reads for each polyomaviridae species
+polyoma_species_counts <- hv_reads_species %>%
+  filter(seq_id %in% polyoma_ids) %>%
+  group_by(sample, date_alias, city, name, taxid) %>%
+  count(name = "n_reads_hv") %>%
+  group_by(sample, date_alias, city) %>%
+  mutate(p_reads_polyoma = n_reads_hv/sum(n_reads_hv))
+
+# Identify high-ranking families and group others
+polyoma_species_major_tab <- polyoma_species_counts %>% group_by(name) %>% 
+  filter(p_reads_polyoma == max(p_reads_polyoma)) %>% 
+  filter(row_number() == 1) %>%
+  arrange(desc(p_reads_polyoma)) %>% 
+  filter(p_reads_polyoma > threshold_major_species)
+polyoma_species_counts_major <- polyoma_species_counts %>%
+  mutate(name_display = ifelse(name %in% polyoma_species_major_tab$name, 
+                               name, "Other")) %>%
+  group_by(sample, date_alias, city, name_display) %>%
+  summarize(n_reads_polyoma = sum(n_reads_hv),
+            p_reads_polyoma = sum(p_reads_polyoma), 
+            .groups="drop") %>%
+  mutate(name_display = factor(name_display, 
+                               levels = c(polyoma_species_major_tab$name, "Other")))
+polyoma_species_counts_display <- polyoma_species_counts_major %>%
+  rename(p_reads = p_reads_polyoma, classification = name_display)
+
+# Plot
+g_polyoma_species <- g_comp_base + 
+  geom_col(data=polyoma_species_counts_display, position = "stack") +
+  scale_y_continuous(name="% Polyomaviridae Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral species") +
+  labs(title="Species composition of Polyomaviridae reads") +
+  guides(fill=guide_legend(ncol=2)) +
+  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))
+
+g_polyoma_species
+
+# Get most prominent species for text
+polyoma_species_collate <- polyoma_species_counts %>% group_by(name, taxid) %>% 
+  summarize(n_reads_tot = sum(n_reads_hv), p_reads_mean = mean(p_reads_polyoma), .groups="drop") %>% 
+  arrange(desc(n_reads_tot))
+```
+
+Finally, poxvirus reads in most samples are dominated by molluscum contagiosum virus (which I expect to be real), followed by Orf virus (which I expect to be fake). These expectations are borne out by BLAST alignments (below).
+
+```{r}
+#| label: hv-species-pox
+#| fig-height: 6
+
+threshold_major_species <- 0.1
+taxid_pox <- viral_taxa %>% filter(name == "Poxviridae") %>% pull(taxid)
+
+# Get set of poxviridae reads
+# Get set of poxviridae reads
+pox_samples <- hv_family_counts %>% filter(taxid == taxid_pox) %>%
+  filter(p_reads_hv >= 0.1) %>%
+  pull(sample)
+pox_ids <- hv_reads_family %>% 
+  filter(taxid == taxid_pox, sample %in% pox_samples) %>%
+  pull(seq_id)
+
+# Count reads for each poxviridae species
+pox_species_counts <- hv_reads_species %>%
+  filter(seq_id %in% pox_ids) %>%
+  group_by(sample, date_alias, city, name, taxid) %>%
+  count(name = "n_reads_hv") %>%
+  group_by(sample, date_alias, city) %>%
+  mutate(p_reads_pox = n_reads_hv/sum(n_reads_hv))
+
+# Identify high-ranking families and group others
+pox_species_major_tab <- pox_species_counts %>% group_by(name) %>% 
+  filter(p_reads_pox == max(p_reads_pox)) %>% 
+  filter(row_number() == 1) %>%
+  arrange(desc(p_reads_pox)) %>% 
+  filter(p_reads_pox > threshold_major_species)
+pox_species_counts_major <- pox_species_counts %>%
+  mutate(name_display = ifelse(name %in% pox_species_major_tab$name, 
+                               name, "Other")) %>%
+  group_by(sample, date_alias, city, name_display) %>%
+  summarize(n_reads_pox = sum(n_reads_hv),
+            p_reads_pox = sum(p_reads_pox), 
+            .groups="drop") %>%
+  mutate(name_display = factor(name_display, 
+                               levels = c(pox_species_major_tab$name, "Other")))
+pox_species_counts_display <- pox_species_counts_major %>%
+  rename(p_reads = p_reads_pox, classification = name_display)
+
+# Plot
+g_pox_species <- g_comp_base + 
+  geom_col(data=pox_species_counts_display, position = "stack") +
+  scale_y_continuous(name="% Poxviridae Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral species") +
+  labs(title="Species composition of Poxviridae reads") +
+  guides(fill=guide_legend(ncol=4)) +
+  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))
+
+g_pox_species
+
+# Get most prominent species for text
+pox_species_collate <- pox_species_counts %>% group_by(name, taxid) %>% 
+  summarize(n_reads_tot = sum(n_reads_hv), p_reads_mean = mean(p_reads_pox), .groups="drop") %>% 
+  arrange(desc(n_reads_tot))
+```
+
+```{r}
+#| label: hv-blast-hits
+#| fig-width: 6
+
+# Configure
+ref_taxids_hv <- c(10279, 10258)
+ref_names_hv <- sapply(ref_taxids_hv, function(x) viral_taxa %>% filter(taxid == x) %>% pull(name) %>% first)
+p_threshold <- 0.1
+
+# Get taxon names
+tax_names_path <- file.path(data_dir_base, "taxid-names.tsv.gz")
+tax_names <- read_tsv(tax_names_path, show_col_types = FALSE)
+
+# Add missing names
+tax_names_new <- tribble(~staxid, ~name,
+                         3050295, "Cytomegalovirus humanbeta5",
+                         459231, "FLAG-tagging vector pFLAG97-TSR",
+                         257877, "Macaca thibetana thibetana",
+                         256321, "Lentiviral transfer vector pHsCXW",
+                         419242, "Shuttle vector pLvCmvMYOCDHA",
+                         419243, "Shuttle vector pLvCmvLacZ",
+                         421868, "Cloning vector pLvCmvLacZ.Gfp",
+                         421869, "Cloning vector pLvCmvMyocardin.Gfp",
+                         426303, "Lentiviral vector pNL-GFP-RRE(SA)",
+                         436015, "Lentiviral transfer vector pFTMGW",
+                         454257, "Shuttle vector pLvCmvMYOCD2aHA",
+                         476184, "Shuttle vector pLV.mMyoD::ERT2.eGFP",
+                         476185, "Shuttle vector pLV.hMyoD.eGFP",
+                         591936, "Piliocolobus tephrosceles",
+                         627481, "Lentiviral transfer vector pFTM3GW",
+                         680261, "Self-inactivating lentivirus vector pLV.C-EF1a.cyt-bGal.dCpG",
+                         2952778, "Expression vector pLV[Exp]-EGFP:T2A:Puro-EF1A",
+                         3022699, "Vector PAS_122122",
+                         3025913, "Vector pSIN-WP-mPGK-GDNF",
+                         3105863, "Vector pLKO.1-ZsGreen1",
+                         3105864, "Vector pLKO.1-ZsGreen1 mouse Wfs1 shRNA",
+                         3108001, "Cloning vector pLVSIN-CMV_Neo_v4.0",
+                         3109234, "Vector pTwist+Kan+High",
+                         3117662, "Cloning vector pLV[Exp]-CBA>P301L",
+                         3117663, "Cloning vector pLV[Exp]-CBA>P301L:T2A:mRuby3",
+                         3117664, "Cloning vector pLV[Exp]-CBA>hMAPT[NM_005910.6](ns):T2A:mRuby3",
+                         3117665, "Cloning vector pLV[Exp]-CBA>mRuby3",
+                         3117666, "Cloning vector pLV[Exp]-CBA>mRuby3/NFAT3 fusion protein",
+                         3117667, "Cloning vector pLV[Exp]-Neo-mPGK>{EGFP-hSEPT6}",
+                         438045, "Xenotropic MuLV-related virus",
+                         447135, "Myodes glareolus",
+                         590745, "Mus musculus mobilized endogenous polytropic provirus",
+                         181858, "Murine AIDS virus-related provirus",
+                         356663, "Xenotropic MuLV-related virus VP35",
+                         356664, "Xenotropic MuLV-related virus VP42",
+                         373193, "Xenotropic MuLV-related virus VP62",
+                         286419, "Canis lupus dingo",
+                         415978, "Sus scrofa scrofa",
+                         494514, "Vulpes lagopus",
+                         3082113, "Rangifer tarandus platyrhynchus",
+                         3119969, "Bubalus kerabau")
+tax_names <- bind_rows(tax_names, tax_names_new)
+
+# Get matches
+hv_blast_staxids <- hv_reads_species %>% filter(taxid %in% ref_taxids_hv) %>%
+  group_by(taxid) %>% mutate(n_seq = n()) %>%
+  left_join(blast_results_paired, by="seq_id") %>%
+  mutate(staxid = as.integer(staxid)) %>%
+  left_join(tax_names %>% rename(sname=name), by="staxid")
+
+# Count matches
+hv_blast_counts <- hv_blast_staxids %>%
+  group_by(taxid, name, staxid, sname, n_seq) %>%
+  count %>% mutate(p=n/n_seq)
+
+# Subset to major matches
+hv_blast_counts_major <- hv_blast_counts %>% 
+  filter(n>1, p>p_threshold, !is.na(staxid)) %>%
+  arrange(desc(p)) %>% group_by(taxid) %>%
+  filter(row_number() <= 25) %>%
+  mutate(name_display = ifelse(name == ref_names_hv[1], "MCV", name))
+
+# Plot
+g_hv_blast <- ggplot(hv_blast_counts_major, mapping=aes(x=p, y=sname)) +
+  geom_col() +
+  facet_grid(name_display~., scales="free_y", space="free_y") +
+  scale_x_continuous(name="% mapped reads", limits=c(0,1), 
+                     breaks=seq(0,1,0.2), expand=c(0,0)) +
+  theme_base + theme(axis.title.y = element_blank())
+g_hv_blast
+```
+
+Finally, here again are the overall relative abundances of the specific viral genera I picked out manually in my last entry:
+
+```{r}
+#| fig-height: 5
+#| label: ra-genera
+
+# Define reference genera
+path_genera_rna <- c("Mamastrovirus", "Enterovirus", "Salivirus", "Kobuvirus", "Norovirus", "Sapovirus", "Rotavirus", "Alphacoronavirus", "Betacoronavirus", "Alphainfluenzavirus", "Betainfluenzavirus", "Lentivirus")
+path_genera_dna <- c("Mastadenovirus", "Alphapolyomavirus", "Betapolyomavirus", "Alphapapillomavirus", "Betapapillomavirus", "Gammapapillomavirus", "Orthopoxvirus", "Simplexvirus",
+                     "Lymphocryptovirus", "Cytomegalovirus", "Dependoparvovirus")
+path_genera <- bind_rows(tibble(name=path_genera_rna, genome_type="RNA genome"),
+                         tibble(name=path_genera_dna, genome_type="DNA genome")) %>%
+  left_join(viral_taxa, by="name")
+
+# Count in each sample
+n_path_genera <- hv_reads_genus %>% 
+  group_by(sample, date_alias, city, name, taxid) %>% 
+  count(name="n_reads_viral") %>% 
+  inner_join(path_genera, by=c("name", "taxid")) %>%
+  left_join(read_counts_raw, by=c("sample", "date_alias", "city")) %>%
+  mutate(p_reads_viral = n_reads_viral/n_reads_raw)
+
+# Pivot out and back to add zero lines
+n_path_genera_out <- n_path_genera %>% ungroup %>% select(sample, name, n_reads_viral) %>%
+  pivot_wider(names_from="name", values_from="n_reads_viral", values_fill=0) %>%
+  pivot_longer(-sample, names_to="name", values_to="n_reads_viral") %>%
+  left_join(read_counts_raw, by="sample") %>%
+  left_join(path_genera, by="name") %>%
+  mutate(p_reads_viral = n_reads_viral/n_reads_raw)
+
+## Aggregate across dates
+n_path_genera_stype <- n_path_genera_out %>% 
+  group_by(name, taxid, genome_type, city) %>%
+  summarize(n_reads_raw = sum(n_reads_raw),
+            n_reads_viral = sum(n_reads_viral), .groups = "drop") %>%
+  mutate(sample="All samples", date="All dates",
+         p_reads_viral = n_reads_viral/n_reads_raw,
+         na_type = "DNA")
+
+# Plot
+g_path_genera <- ggplot(n_path_genera_stype,
+                        aes(y=name, x=p_reads_viral, color=city)) +
+  geom_point() +
+  scale_x_log10(name="Relative abundance") +
+  scale_color_city() +
+  facet_grid(genome_type~., scales="free_y") +
+  theme_base + theme(axis.title.y = element_blank())
+g_path_genera
+```
+
+# Conclusion
+
+This is the third, largest, and final of this tranche of air-sampling datasets that I've run through this pipeline. Many of the high-level findings were similar to Prussin and Rosario, including high relative abundance of human reads, low total viral reads, an absence of enteric viruses, and high abundance of papillomaviruses among human-infecting viruses.
+
+In the future, I'll do a more in-depth comparative analysis across different datasets to compare the abundance of different viruses. For now, though, there are some major updates to the pipeline I want to make before I do any more public analyses.
diff --git a/notebooks/2024-04-12_rosario.qmd b/notebooks/2024-04-12_rosario.qmd
index 14078c2..241e65c 100644
--- a/notebooks/2024-04-12_rosario.qmd
+++ b/notebooks/2024-04-12_rosario.qmd
@@ -32,7 +32,7 @@ theme_kit <- theme_rotate + theme(
 tnl <- theme(legend.position = "none")
 ```
 
-Continuing our look at air sampling datasets, we turn to Rosario et al. (2018), another study of air filters, this time from HVAC filters from an undergraduate dorm building at the University of Colorado campus in Boulder. As in [Prussin](https://data.securebio.org/wills-public-notebook/notebooks/2024-04-12_prussin.html), samples were eluted from filters (in this case MERV-8, so less stringent than Prussin's MERV-14 filters) and underwent both RNA and DNA sequencing – this time on an Illumina MiSeq with 2x250bp reads.
+Continuing our look at air sampling datasets, we turn to [Rosario et al. (2018)](https://pubs.acs.org/doi/10.1021/acs.est.7b04203), another study of air filters, this time from HVAC filters from an undergraduate dorm building at the University of Colorado campus in Boulder. As in [Prussin](https://data.securebio.org/wills-public-notebook/notebooks/2024-04-12_prussin.html), samples were eluted from filters (in this case MERV-8, so less stringent than Prussin's MERV-14 filters) and underwent both RNA and DNA sequencing – this time on an Illumina MiSeq with 2x250bp reads.
 
 # The raw data